I am a fourth-year Ph.D. candidate in Computer Science at Stanford University, where I am advised by Stefano Ermon and affiliated with the Stanford Artificial Intelligence Laboratory and the Statistical Machine Learning Group. My research focusses on various aspects of machine learning, including probabilistic modeling, stochastic optimization, and deep learning. Previously, I spent two summers interning at OpenAI (2017) and Microsoft Research, Redmond (2018). Before joining Stanford, I obtained my bachelors in Computer Science and Engineering from IIT Delhi (2015).
I am a recipient of the Microsoft Research PhD Fellowship in machine learning, the Stanford Data Science Scholarship, and the Stanford Teaching Fellowship. As a Stanford Teaching Fellow, I recently taught a new class on Deep Generative Models in 2018 with an enrollment of 150+ students.
I am interested in developing algorithms for efficient learning and inference in probabilistic models. A large part of my research in this direction entails the design and analysis of suitable learning objectives, stochastic optimization algorithms, and representation frameworks for probabilistic reasoning (ICLR 2019, AISTATS 2019a, 2018a,b, AAAI 2018a,b). These endeavors have often led to algorithms that bridge theory and practice for applications across machine learning, e.g., fair representation learning, constraint satisfaction problems, compressed sensing, and multiagent reinforcement learning (AISTATS 2019b, NeurIPS 2018, ICML 2018a,b).