I am a third year PhD candidate at NYU working with Prof. Rajesh Ranganath. My current research focuses on robustness guarantees across populations for modern machine learning models using insights and techniques from causal inference. In recent work, we tackled causal effect estimation when standard assumptions like ignorability and positivity/overlap do not hold. I also work on improving calibration for survival models used for disease risk prediction. My primary applied interest is data-driven support and decision-making for healthcare in particular, and science in general.
I’m eternally excited about new ideas and finding good applications for causal inference! Shoot me an email if you want to chat!
I was an intern in the summer of 2019 in Adobe Research, San Jose working on bayesian attribution models for ad targeting. Previously, I worked as a Software Developer at DBMI, Columbia University. In 2017, I completed my MS in CS, also at NYU. I was introduced to causal inference in the Clinical Machine Learning group, where I worked with two amazing mentors, Prof. Uri Shalit and Prof. David Sontag. My fateful but fun undergrad was from IIT Madras, where I was enrolled in the EE department.