LeakIX - Host yinjunphua.com

Open service 2a06:98c1:3121::3:443 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 200 OK
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Type: text/html; charset=utf-8
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Connection: close
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Page title: Yin Jun & Reina

<!doctype html>
<html>
<!-- Handcrafted with love -->
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1">
  <link href="/output.css" rel="stylesheet">
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
  <link rel="preconnect" href="https://maps.googleapis.com">
  <link href="https://fonts.googleapis.com/css2?family=Explora&family=Felipa&display=swap" rel="stylesheet">
  <script async src="https://maps.googleapis.com/maps/api/js?key=AIzaSyA5PST2icMw-LMcr7CueOg8IEDe_-7ZJjU&callback=console.debug&libraries=maps,marker&v=beta"></script>
  <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
  <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
  <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
  <link rel="manifest" href="/site.webmanifest">
  <title>Yin Jun &amp; Reina</title>
  <script type="text/javascript" src="/invitation.js"></script>
</head>
<body class="relative">
  <div class="bg-white">
    <div class="relative isolate px-6 lg:pt-14 lg:px-8">
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          <picture>
            <source srcset="/IMG_6646.webp" type="image/webp">
            <img src="/IMG_6646.png" />
          </picture>
        </div>
      </div>
      <div class="mx-auto max-w-2xl sm:py-16 py-8 lg:py-56 md:py-32">
        <div class="text-center">
          <h1 class="mt-4 text-4xl font-bold tracking-tight text-slate-100 sm:text-6xl felipa-regular" style="text-shadow: 1px 1px 2px black">Yin Jun &amp; Reina</h1>
          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
        </div>
      </div>
    </div>
    <div class="flex flex-col">
      <section>
        <div class="bg-white relative overflow-hidden">
          <div class="border-4 border-orange-200 m-8 py-8 md:py-32">
            <div class="mx-auto py-8 text-center">
              <h2 class="text-md text-3xl felipa-regular">Message</h2>
            </div>
            <div class="mx-auto py-8">
              <p class="text-center md:hidden">English follows Japanese.</p>
              <div class="mx-auto p-8 md:p-14 max-w-2xl font-serif md:flex">
                <div class="md:w-2/5 w-full">
                  <p class="mb-8">拝啓</p>
                  <p>皆様いかがお過ごしでしょうか</p>
                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
                  <p>It is with immense pleasure and gratitude that</p>
                  <p>Phua Yin Jun</p>
                  <p>and</p>
                  <p>Tsubakihara Reina</p>
                  <p>would like to invite you</p>
                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

Found 2026-01-09 by HttpPlugin

Create report

Open service 188.114.97.3:80 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 301 Moved Permanently
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Length: 0
Connection: close
Location: https://wedding.yinjunphua.com/
Vary: accept-encoding
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cf-cache-status: DYNAMIC
CF-RAY: 9bb70804db0bf8e0-SIN
alt-svc: h3=":443"; ma=86400

Found 2026-01-09 by HttpPlugin

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Open service 2a06:98c1:3120::3:80 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 301 Moved Permanently
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Length: 0
Connection: close
Location: https://wedding.yinjunphua.com/
Vary: accept-encoding
Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=DgDG9IHxKNqlbBPw4iy4VtAt%2FFCo8rhypYpdzczn5ZLdQMkXHcS2r3pBz%2F%2Fp%2F864oSpr5eFrsxsuWd8y%2F7a%2BheWOh5EpXyda2ieO3PiQvBfwu1ycIJE5AKnULJasMngRyrU%3D"}]}
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Server: cloudflare
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cf-cache-status: DYNAMIC
CF-RAY: 9bb708052cf58c81-EWR
alt-svc: h3=":443"; ma=86400

Found 2026-01-09 by HttpPlugin

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Open service 2a06:98c1:3121::3:8443 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 200 OK
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Type: text/html; charset=utf-8
Transfer-Encoding: chunked
Connection: close
Access-Control-Allow-Origin: *
Cache-Control: public, max-age=0, must-revalidate
Link: <https://fonts.googleapis.com>; rel="preconnect", <https://maps.googleapis.com>; rel="preconnect"
referrer-policy: strict-origin-when-cross-origin
x-content-type-options: nosniff
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Server: cloudflare
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alt-svc: h3=":8443"; ma=86400

Page title: Yin Jun & Reina

<!doctype html>
<html>
<!-- Handcrafted with love -->
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1">
  <link href="/output.css" rel="stylesheet">
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
  <link rel="preconnect" href="https://maps.googleapis.com">
  <link href="https://fonts.googleapis.com/css2?family=Explora&family=Felipa&display=swap" rel="stylesheet">
  <script async src="https://maps.googleapis.com/maps/api/js?key=AIzaSyA5PST2icMw-LMcr7CueOg8IEDe_-7ZJjU&callback=console.debug&libraries=maps,marker&v=beta"></script>
  <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
  <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
  <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
  <link rel="manifest" href="/site.webmanifest">
  <title>Yin Jun &amp; Reina</title>
  <script type="text/javascript" src="/invitation.js"></script>
</head>
<body class="relative">
  <div class="bg-white">
    <div class="relative isolate px-6 lg:pt-14 lg:px-8">
      <div class="absolute inset-x-0 -z-10 transform-gpu blur-sm overflow-hidden min-[320px]:-top-20 md:-top-40 sm:-top-20" aria-hidden="true">
        <div class="relative w-screen">
          <picture>
            <source srcset="/IMG_6646.webp" type="image/webp">
            <img src="/IMG_6646.png" />
          </picture>
        </div>
      </div>
      <div class="mx-auto max-w-2xl sm:py-16 py-8 lg:py-56 md:py-32">
        <div class="text-center">
          <h1 class="mt-4 text-4xl font-bold tracking-tight text-slate-100 sm:text-6xl felipa-regular" style="text-shadow: 1px 1px 2px black">Yin Jun &amp; Reina</h1>
          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
        </div>
      </div>
    </div>
    <div class="flex flex-col">
      <section>
        <div class="bg-white relative overflow-hidden">
          <div class="border-4 border-orange-200 m-8 py-8 md:py-32">
            <div class="mx-auto py-8 text-center">
              <h2 class="text-md text-3xl felipa-regular">Message</h2>
            </div>
            <div class="mx-auto py-8">
              <p class="text-center md:hidden">English follows Japanese.</p>
              <div class="mx-auto p-8 md:p-14 max-w-2xl font-serif md:flex">
                <div class="md:w-2/5 w-full">
                  <p class="mb-8">拝啓</p>
                  <p>皆様いかがお過ごしでしょうか</p>
                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
                  <p>It is with immense pleasure and gratitude that</p>
                  <p>Phua Yin Jun</p>
                  <p>and</p>
                  <p>Tsubakihara Reina</p>
                  <p>would like to invite you</p>
                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

Found 2026-01-09 by HttpPlugin

Create report

Open service 2a06:98c1:3120::3:443 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 200 OK
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Type: text/html; charset=utf-8
Transfer-Encoding: chunked
Connection: close
Access-Control-Allow-Origin: *
Cache-Control: public, max-age=0, must-revalidate
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referrer-policy: strict-origin-when-cross-origin
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CF-RAY: 9bb70804b951f795-EWR
alt-svc: h3=":443"; ma=86400

Page title: Yin Jun & Reina

<!doctype html>
<html>
<!-- Handcrafted with love -->
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1">
  <link href="/output.css" rel="stylesheet">
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
  <link rel="preconnect" href="https://maps.googleapis.com">
  <link href="https://fonts.googleapis.com/css2?family=Explora&family=Felipa&display=swap" rel="stylesheet">
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  <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
  <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
  <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
  <link rel="manifest" href="/site.webmanifest">
  <title>Yin Jun &amp; Reina</title>
  <script type="text/javascript" src="/invitation.js"></script>
</head>
<body class="relative">
  <div class="bg-white">
    <div class="relative isolate px-6 lg:pt-14 lg:px-8">
      <div class="absolute inset-x-0 -z-10 transform-gpu blur-sm overflow-hidden min-[320px]:-top-20 md:-top-40 sm:-top-20" aria-hidden="true">
        <div class="relative w-screen">
          <picture>
            <source srcset="/IMG_6646.webp" type="image/webp">
            <img src="/IMG_6646.png" />
          </picture>
        </div>
      </div>
      <div class="mx-auto max-w-2xl sm:py-16 py-8 lg:py-56 md:py-32">
        <div class="text-center">
          <h1 class="mt-4 text-4xl font-bold tracking-tight text-slate-100 sm:text-6xl felipa-regular" style="text-shadow: 1px 1px 2px black">Yin Jun &amp; Reina</h1>
          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
        </div>
      </div>
    </div>
    <div class="flex flex-col">
      <section>
        <div class="bg-white relative overflow-hidden">
          <div class="border-4 border-orange-200 m-8 py-8 md:py-32">
            <div class="mx-auto py-8 text-center">
              <h2 class="text-md text-3xl felipa-regular">Message</h2>
            </div>
            <div class="mx-auto py-8">
              <p class="text-center md:hidden">English follows Japanese.</p>
              <div class="mx-auto p-8 md:p-14 max-w-2xl font-serif md:flex">
                <div class="md:w-2/5 w-full">
                  <p class="mb-8">拝啓</p>
                  <p>皆様いかがお過ごしでしょうか</p>
                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
                  <p>It is with immense pleasure and gratitude that</p>
                  <p>Phua Yin Jun</p>
                  <p>and</p>
                  <p>Tsubakihara Reina</p>
                  <p>would like to invite you</p>
                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

Found 2026-01-09 by HttpPlugin

Create report

Open service 2a06:98c1:3121::3:80 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 301 Moved Permanently
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Length: 0
Connection: close
Location: https://wedding.yinjunphua.com/
Vary: accept-encoding
Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=Nn58EtYqd5B9YXwA4bwS%2BFa9p0dDfH32QhWda7cZ6gdhXjfcXIsDV7hhYxNiF1Owuq8OUoVQ%2FA8%2BdLj7yRhn65MYoWuzMiqlMJ3od%2Fe0v%2BquxzwO4W%2BiUgBKsN3MjiUO4dI%3D"}]}
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Server: cloudflare
cf-cache-status: DYNAMIC
CF-RAY: 9bb7080489c3d396-FRA
alt-svc: h3=":443"; ma=86400

Found 2026-01-09 by HttpPlugin

Create report

Open service 188.114.97.3:8443 · wedding.yinjunphua.com

2026-01-09 21:29

HTTP/1.1 200 OK
Date: Fri, 09 Jan 2026 21:29:39 GMT
Content-Type: text/html; charset=utf-8
Transfer-Encoding: chunked
Connection: close
Access-Control-Allow-Origin: *
Cache-Control: public, max-age=0, must-revalidate
cf-cache-status: DYNAMIC
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referrer-policy: strict-origin-when-cross-origin
x-content-type-options: nosniff
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CF-RAY: 9bb70806a8f05fbb-SIN
alt-svc: h3=":8443"; ma=86400

Page title: Yin Jun & Reina

<!doctype html>
<html>
<!-- Handcrafted with love -->
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1">
  <link href="/output.css" rel="stylesheet">
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
  <link rel="preconnect" href="https://maps.googleapis.com">
  <link href="https://fonts.googleapis.com/css2?family=Explora&family=Felipa&display=swap" rel="stylesheet">
  <script async src="https://maps.googleapis.com/maps/api/js?key=AIzaSyA5PST2icMw-LMcr7CueOg8IEDe_-7ZJjU&callback=console.debug&libraries=maps,marker&v=beta"></script>
  <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
  <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
  <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
  <link rel="manifest" href="/site.webmanifest">
  <title>Yin Jun &amp; Reina</title>
  <script type="text/javascript" src="/invitation.js"></script>
</head>
<body class="relative">
  <div class="bg-white">
    <div class="relative isolate px-6 lg:pt-14 lg:px-8">
      <div class="absolute inset-x-0 -z-10 transform-gpu blur-sm overflow-hidden min-[320px]:-top-20 md:-top-40 sm:-top-20" aria-hidden="true">
        <div class="relative w-screen">
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          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
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                  <p class="mb-8">拝啓</p>
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                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
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                  <p>and</p>
                  <p>Tsubakihara Reina</p>
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                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

Found 2026-01-09 by HttpPlugin

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Open service 188.114.97.3:443 · wedding.yinjunphua.com

2026-01-09 21:29

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Page title: Yin Jun & Reina

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          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
        </div>
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          <div class="border-4 border-orange-200 m-8 py-8 md:py-32">
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              <div class="mx-auto p-8 md:p-14 max-w-2xl font-serif md:flex">
                <div class="md:w-2/5 w-full">
                  <p class="mb-8">拝啓</p>
                  <p>皆様いかがお過ごしでしょうか</p>
                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
                  <p>It is with immense pleasure and gratitude that</p>
                  <p>Phua Yin Jun</p>
                  <p>and</p>
                  <p>Tsubakihara Reina</p>
                  <p>would like to invite you</p>
                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

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Open service 2a06:98c1:3120::3:8443 · wedding.yinjunphua.com

2026-01-09 21:29

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Page title: Yin Jun & Reina

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<body class="relative">
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          <h1 class="mt-4 text-4xl font-bold tracking-tight text-slate-100 sm:text-6xl felipa-regular" style="text-shadow: 1px 1px 2px black">Yin Jun &amp; Reina</h1>
          <p class="mt-16 text-lg leading-8 text-slate-100 font-serif" style="text-shadow: 1px 1px 2px black">素敵な一日</p>
        </div>
      </div>
    </div>
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          <div class="border-4 border-orange-200 m-8 py-8 md:py-32">
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              <h2 class="text-md text-3xl felipa-regular">Message</h2>
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            <div class="mx-auto py-8">
              <p class="text-center md:hidden">English follows Japanese.</p>
              <div class="mx-auto p-8 md:p-14 max-w-2xl font-serif md:flex">
                <div class="md:w-2/5 w-full">
                  <p class="mb-8">拝啓</p>
                  <p>皆様いかがお過ごしでしょうか</p>
                  <p>このたび結婚式を</p>
                  <p>執り行うこととなりました</p>
                  <p class="mt-4">日頃お世話になっております皆様に</p>
                  <p>私どもの門出を</p>
                  <p>お見守りいただきたく</p>
                  <p>ささやかながら小宴を</p>
                  <p>催したく存じます</p>
                  <p class="mt-4">ご多用中誠に恐縮ではございますが</p>
                  <p>ぜひご出席いただきたく</p>
                  <p>ご案内申し上げます</p>
                  <p class="my-8">敬具</p>
                  <p>ポアインジュン・椿原怜奈</p>
                </div>
                <div class="md:w-2/5 md:mt-0 mt-12 w-full md:ml-16 lg:ml-16 italic">
                  <p>It is with immense pleasure and gratitude that</p>
                  <p>Phua Yin Jun</p>
                  <p>and</p>
                  <p>Tsubakihara Reina</p>
                  <p>would like to invite you</p>
                  <p>to share a special moment of our lives.</p>
                  <p class="mt-4">We greatly look forward</p>
                  <p>to having the honor of your presence</p>
                  <p>as we write our own love story</p>
                  <p class="mt-4">We hope that</p>
                  <p>you will join us</p>
                  <p>and make our joy complete</p>

Found 2026-01-09 by HttpPlugin

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Open service 2600:9000:20ae:8800:1c:e11a:8300:93a1:80 · www.yinjunphua.com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Open service 2600:9000:20ae:c000:1c:e11a:8300:93a1:443 · www.yinjunphua.com

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Connection: close
Date: Fri, 09 Jan 2026 07:26:21 GMT
Last-Modified: Wed, 24 Dec 2025 04:56:56 GMT
ETag: "3ef6ffaac228d30a732239a7cf342964"
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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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  <title>Yin Jun, Phua -- Assistant Professor at Science Tokyo</title>
  
  
  
  
  <meta name="description" content="Yin Jun Phua, currently an assistant professor at Institute of Science Tokyo. Main research focus is on bridging the gap between symbolic AI and neural networks.">
  
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  <nav class="nav">
    <div class="nav-left">
      
      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
    <div class="nav-right">
      
      <a href="/my-research.html">My Research</a>
      
    </div>
  </nav>
  <hr>
  <div class="row">
  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
    <picture>
      <source srcset="/images/profile_2023.webp" type="image/webp">
      <source srcset="/images/profile_2023.png" type="image/png">
      <img src="/images/profile_2023.png" alt="My Profile Picture 2023">
    </picture>
  </div>
</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Open service 2600:9000:20ae:3000:1c:e11a:8300:93a1:80 · www.yinjunphua.com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
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    </div>
  </nav>
  <hr>
  <div class="row">
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
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      <a href="/my-research.html">My Research</a>
      
    </div>
  </nav>
  <hr>
  <div class="row">
  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
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      <a href="/my-research.html">My Research</a>
      
    </div>
  </nav>
  <hr>
  <div class="row">
  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
    <picture>
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  </div>
</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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  <hr>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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    </div>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
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  <hr>
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
    </div>
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  <hr>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
  </div>
  <div class="col-2">
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    </div>
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  <div class="col-10">
    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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      <h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
      
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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
    <p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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    <p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
    <p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
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    </picture>
  </div>
</div>
<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
<h3>Goal</h3>
<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, com

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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	<title>Yin Jun, Phua -- Assistant Professor at Science Tokyo</title>
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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
					<source srcset="/images/profile_2023.png" type="image/png">
					<img src="/images/profile_2023.png" alt="My Profile Picture 2023">
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			</div>
		</div>
		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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	<title>Yin Jun, Phua -- Assistant Professor at Science Tokyo</title>
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			<div class="nav-left">
				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
					<source srcset="/images/profile_2023.png" type="image/png">
					<img src="/images/profile_2023.png" alt="My Profile Picture 2023">
				</picture>
			</div>
		</div>
		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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<head>
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	<title>Yin Jun, Phua -- Assistant Professor at Science Tokyo</title>
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		<nav class="nav">
			<div class="nav-left">
				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
					<source srcset="/images/profile_2023.png" type="image/png">
					<img src="/images/profile_2023.png" alt="My Profile Picture 2023">
				</picture>
			</div>
		</div>
		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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Page title: Yin Jun, Phua -- Assistant Professor at Science Tokyo

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
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				<a href="/my-research.html">My Research</a>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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			<div class="col-2">
				<picture>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
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				<a href="/my-research.html">My Research</a>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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				<picture>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
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				<a href="/my-research.html">My Research</a>
			</div>
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		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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			<div class="col-2">
				<picture>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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			<div class="nav-left">
				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
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					<img src="/images/profile_2023.png" alt="My Profile Picture 2023">
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
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				<a href="/my-research.html">My Research</a>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
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				<a href="/my-research.html">My Research</a>
			</div>
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		<hr>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
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		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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			<div class="col-2">
				<picture>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
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				<a href="/my-research.html">My Research</a>
			</div>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
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		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
			</div>
			<div class="nav-right">
				<a href="/my-research.html">My Research</a>
			</div>
		</nav>
		<hr>
		<div class="row">
			<div class="col-10">
				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
			</div>
			<div class="col-2">
				<picture>
					<source srcset="/images/profile_2023.webp" type="image/webp">
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					<img src="/images/profile_2023.png" alt="My Profile Picture 2023">
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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				<h1 class="is-marginless">Yin Jun, Phua <small>&mdash; Ph.D. in Informatics</small></h1>
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				<p><span class="text-grey">Currently</span> <strong>Assistant Professor</strong> <span class="text-grey">at</span> Institute of Science Tokyo <span class="text-grey">since</span> October, 2024.</p>
				<p><span class="text-grey">My main research focus is on</span> bridging the gap between <strong>symbolic AI</strong> and <strong>neural networks</strong>.</p>
				<p class="text-grey">During my Ph.D., I focused on developing deep learning techniques that could extract symbolic rules from time series data. The symbolic rules generated by these techniques were meant to serve as an explanation for the observed data, and could be used for prediction and manipulation of dynamic systems. My work aimed to create explainable AI systems that could provide human-accessible insights into complex systems.</p>
				<p class="text-grey">Currently, I am exploring a new technique that combines the strengths of both symbolic and neural networks. While symbolic AI provides insights that are directly transferable and learnable by humans, its brittleness limits its real-world application. On the other hand, neural networks are robust against noise and have broad real-world applications, but the knowledge and insights they provide can be difficult to access. By combining both approaches, I hope to devise intelligent systems that are both robust and accessible to humans. My goal is to create explainable AI systems that can provide valuable insights into complex systems while also being practical for real-world applications.</p>
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		<p><span class="text-grey">Research areas: </span>deep learning, symbolic rule extraction, time series analysis, explainable AI, hybrid approach, neural networks, robustness, and human-accessible insights.</p>
		<p class="text-grey">Have interesting projects or want to have some discussion? You can contact me at <span class="text-dark">phua<span class="replace"> [at] </span>comp.isct.ac.jp</span></p>
		<h3>Goal</h3>
		<p class="text-grey">My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both approaches to create more <strong class="text-dark">robust</strong> and <strong class="text-dark">flexi

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