I am a PhD student in the CS Department at UC San Diego where I extremely fortunate to be adivsed by Prof. Julian McAuley. My (current) research interests in Machine Learning are Data Mining, NLP, Information Retrieval, Causal Inference, and Reinforcement learning (in no particular order).
Previously, I have worked on building better algorithms for recommender systems, especially for music sharing platforms [RecSys ‘18] [ECIR ‘18], by using the temporal information present in user listening-sequences.
I am fortunate to have the opportunities of working with some of the pioneers in Machine Learning research. At the National Research Council of Italy (ICAR-CNR), I extended on my previous research on sequential recommender systems [WSDM ‘19]. At Cornell, my research on broader topics Causal Inference and Reinforcement Learning, focussed on proposing new estimators for off-policy learning under deficient support [KDD ‘20]. At UCSD, I found an intriguing problem in most of the current review based recommender systems, which I aimed to generalize and provide solutions for [SIGIR ‘20]. More recently, I have been working at Microsoft Research, exploring and working towards improving current state-of-art in extreme classification, a field of high utility in most real-world machine learning applications.
I like to play the guitar, piano, video games, and read books during my free time.
B.Tech + M.S in Computer Science, 2015 - 2020
PhD in Computer Science, 2020 - Present
UC San Diego