I am a Postdoctoral Research Fellow in the Department of Health Care Policy at Harvard University, working with Prof. Alex Luedtke.
I completed my PhD in Statistics and Data Science from the Department of Statistics & Data Science at The Wharton School, University of Pennsylvania, where I was fortunate to be advised by Prof. Dylan Small. During my time at Penn, I have also had the opportunity to collaborate closely with Prof. Eric Tchetgen Tchetgen.
Prior to Penn, I received my Bachelor's and Master's degrees in Statistics from the Indian Statistical Institute, Kolkata.
My research primarily focuses on causal inference, and asking counterfactual questions is a quick way to grab my attention. Specifically, I am interested in:
- Sensitivity analysis to unmeasured confounding
- Double-debiased machine learning (DML)
- Causal inference under network interference
- Heterogeneity of treatment effects
- Applying causal inference to address real-world problems
Beyond causal inference, I am also interested in anytime-valid inference, differential privacy, selective inference, and inference with strategic agents.
I have spent two summers at Amazon during my PhD: in 2023, I developed anytime-valid DML for causal inference, and in 2024, I used proximal causal inference to design better loss functions for regional demand forecasting. My current research and publications can be found here.
When I’m not tangled up in causal riddles, you can find me curled up with a good book, experimenting in the kitchen, or hunched over a game of chess.