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    2026-02-06 11:16

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    Page title: VENKATAKRISHNAN RANGANATHAN - AI Engineer
    
    <!DOCTYPE html>
    <html lang="en">
    
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
        <title>VENKATAKRISHNAN RANGANATHAN - AI Engineer</title>
        <link rel="stylesheet" href="css/bootstrap.min.css">
        <link rel="stylesheet" href="css/styles.css">
    </head>
    
    <body>
    
        <div class="container mt-5">
    
            <header class="text-center mb-5">
                <h1>VENKATAKRISHNAN RANGANATHAN</h1>
                <h2>AI Engineer</h2>
            </header>
    
            <section id="contact" class="mb-5">
                <h3>Contact Information</h3>
                <ul class="list-unstyled">
                    <li>Phone: (+1) 773-516-7471</li>
                    <li>Email: venkatakrishnanr@u.northwestern.edu</li>
                    <li>LinkedIn: <a href="https://linkedin.com/in/venkat-ranganathan">venkat-ranganathan</a></li>
                    <li>GitHub: <a href="https://github.com/venky180">venky180</a></li>
                </ul>
            </section>
    
            <section id="education" class="mb-5">
                <h3>Education</h3>
                <h4>Master of Science (M.S), Artificial Intelligence</h4>
                <p>Northwestern University Evanston, Illinois, December 2023</p>
                <p>GPA: 3.87 / 4.00</p>
                <p>Relevant Coursework: Deep Learning, NLP, Logic in AI, Python, Data Science, Approximation Algorithms, Generative Deep Models, Computer Vision</p>
    
                <h4>Bachelor of Technology (B. Tech), Information and Communication Technology</h4>
                <p>SASTRA University Thanjavur, India, 2010</p>
                <p>GPA: 7.47 / 10.00</p>
            </section>
    
            <section id="skills" class="mb-5">
                <h3>Skills</h3>
                <ul class="list-unstyled">
                    <li>Programming Languages: Python, C++, Java</li>
                    <li>Machine Learning Libraries: PyTorch, TensorFlow, scikit-learn</li>
                    <li>Data Analysis, Data Modeling</li>
                    <li>Deep Learning, Generative Modelling</li>
                    <li>Natural Language Processing (NLP)</li>
                    <li>Cloud Platforms: AWS, Azure, GCP</li>
                    <li>Infrastructure as Code: Terraform</li>
                    <li>Operating Systems: Linux</li>
                    <li>Data Visualization: Power BI, Grafana</li>
                </ul>
            </section>
    
            <section id="recent-projects" class="mb-5">
                <h3>Recent Projects</h3>
    
                <div class="project mb-4">
                    <h4>Analyzing the impact of fine-tuning for summarization of longer sequences in PEGASUS, 2023</h4>
                    <ul class="list-unstyled">
                        <li>Investigated the performance of fine-tuning using the Multi-News dataset for varying input lengths, specifically focusing on the impact of exceeding the maximum input length.</li>
                        <li>Measured advancements in summarization systems and gained a comprehensive understanding of the effects of fine-tuning on lengthier sequences.</li>
                    </ul>
                </div>
    
                <div class="project mb-4">
                    <h4>Legal Case Consolidation using Unsupervised Methods, 2023</h4>
                    <ul class="list-unstyled">
                        <li>The objective involved grouping legal cases that had traversed different hierarchical levels within the courts.</li>
                        <li>Employed unsupervised methods such as TF-IDF, Sentence Transformers, and Legal-NER while utilizing vectorization and semantic similarity search techniques like Cosine Similarity and FAISS.</li>
                        <li>Achieved 90% accuracy on grouping cases based on tests conducted on data from LEXIS.</li>
                    </ul>
                </div>
    
                <div class="project mb-4">
                    <h4>Fake Bio Detection using FFN, LSTM and Transformers, 2023</h4>
                    <ul class="list-unstyled">
                        <li>To detect fake biographies generated by a language model from a mixed corpus of real and fake biographies.</li>
                        <li>Adapted BERT model with 110 million parameters to 
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