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I am a final year Computer Science PhD student at Zarlab, UCLA 🎓. Prior to this, I spent 4 wonderful years at the Indian Institute of Technology, Delhi (IIT Delhi) studying Biochemical Engineering and Biotechnology 🧬 and Computer Science 💻. During my undergrad, I was fortunate to have worked with Professor Frank Doyle (Harvard SEAS) and Professor Eleazar Eskin (UCLA) 👨🏫. I'm really excited about ML models, and more recently Large Language Models (LLMs) in the healthcare and genetics space 🤖! Seeing algorithms I've developed help identify high-risk patients is something that really motivates me 💡. I am on the job market (2025) and looking for industrial research positions. Do not hesitate to reach out to me if you think I am a good fit! |
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I am driven by uncovering insights from complex data, with a specific focus on genetics and healthcare during my PhD. Each summer, I make it a point to engage in cross-domain ML applications, which has led me to explore fields like options trading at Belvedere and instructional finetuning for supply chain optimization at Amazon Research. My research is driven by the goal of leveraging advanced machine learning techniques to bridge the gap between theory and practical applications, ultimately improving real-world outcomes in healthcare and beyond. |
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Varuni Sarwal, Seungmo Lee, Rosemary He, Aingela Kattapuram, Eleazar Eskin, Wei Wang, Serghei Mangul ICML, 2024 Created BioinformaticsBench, which is to date the largest benchmark set containing 602 human annotated questions across 9 different bioinformatics domains. |
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Varuni Sarwal, Seungmo Lee, Jianzhi Yang, Sriram Sankararaman, Mark Chaisson, Eleazar Eskin, Serghei Mangul ASHG, RECOMB, 2023 Created Variant Identification and Structural Variant Analysis (VISTA), a consensus based structural variant caller that identifies SV events with high accuracy and beats SOTA. |
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Varuni Sarwal, Aditya Pimplaskar, Jeffery Chiang, Loes Olde Loohuis Amazon Sciencehub, 2023 Leveraged the power of clinical electronic health record data and machine learning methods to make accurate predictions of perinatal depression, defined as a depressive illness occurring during pregnancy or following childbirth |
A comprehensive list of publications can be found on Google Scholar
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