Background

I am an Applied Scientist at Amazon Web Services (AWS), where I am part of the AI Research and Education (AIRE) group. At AWS, my work involves research related to developing new techniques for scaling up language model pretraining through the utilization of sparse architectures, and devising algorithms and strategies to enhance the data efficiency of model training. Before joining AWS, I contributed both as an ML engineer and researcher in the areas of search and recommendations at Twitter in San Francisco. I was part of the Tweet Search Ranking team where I worked on prototyping and deploying Twitter's first content based search relevance model utilizing explicit user survey feedback in Twitter's Search service.

I obtained my PhD in computer science at the Arizona State University, Tempe. I was advised by Paulo Shakarian. My thesis focused on measuring the impact of social network interactions using observational and experimental studies. During my graduate studies, I had the opportunity to spend summers at Amazon A9 in Palo Alto and Nokia Bell Labs in New Jersey. Even earlier, I had a brief stint at Deloitte's India offices.

I live in and work remotely from San Francisco, California. I finished my undergraduate studies at the Indian Institute of Engineering Science and Technology (IIEST), Shibpur.

Research Interests

My research interests include topics in large-scale machine learning, including data and model efficient pretraining and distributed optimization, computational social science, and their applications in search and recommendation systems. I also enjoy doing independent research in the fields at the intersection of economics and machine learning, mainly with decision making in peer lending platforms and reinforcement learning.

You can find an updated list of published papers and preprints in my Google Scholar page. Please feel free to reach out to me using my email on anything related to my research, paper reviews or any collaborations. For more detailed information on my past work, please check the Interests section in the navigation bar.

News

• Check out our recent papers on using LLMs for tasks with table data - HYTREL: Hypergraph-enhanced Tabular Data Representation Learning (code and models) accepted in NeurIPS 2023 and Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks (code and models to be released) accepted in NeurIPS Table Representation Workshop 2023.
• I am currently co-editing a research topic in Frontiers in Machine Learning for Science of Science and Innovations. If your work involves AI in the realm of Science of Science, Scientific NLP, or the Science of Collaborations and Teams, among other areas, please consider submitting your research on this topic.