Rohit Bhattacharya

Mere data makes a man —
A and C and T and G.
The alphabet of you —
All from four symbols.
I am only two — 1 and 0.

I am an Assistant Professor of Computer Science at Williams College. I develop statistical and machine learning methods for causal inference. I also study the application of these methods to problems in computational genomics with the goal of improving patient outcomes.

I completed my BS in Biomedical Engineering and PhD in Computer Science from Johns Hopkins University where I was advised by Ilya Shpitser.

Email: rb17[at]williams.edu

Twitter: r0ntu

News:

Mar 5, 2021: The paper on differentiable causal discovery in the presence of unmeasured confounders has been accepted at AISTATS 2021 -- camera-ready and code.
Jan 22, 2021: I accepted a tenure-track positition as Assistant Professor of Computer Science at Williams College!
Oct 15, 2020: New draft on differentiable causal discovery in the presence of unmeasured confounders on arXiv.
May 1, 2020: Public release of Ananke: a Python software package for causal inference using graphical models.
Apr 16, 2020: Razieh and I gave a joint talk on semiparametric inference for causal effects in graphical models with hidden variables at the JHU Biostatistics Causal Inference Seminar.
Jan 15, 2020: Razieh and my introductory course on Causal Inference was featured in the Johns Hopkins Hub magazine!
May 25, 2019: Razieh and I won the Thomas R. Ten Have award at ACIC, Montréal. This means that we will be giving a joint talk at ACIC next year in Austin, Texas.
May 22, 2019: Presenting a poster on identification in missing data models represented by directed acyclic graphs at ACIC, Montréal.
May 22, 2019: Presenting a poster on causal inference under interference and network uncertainty at ACIC, Montréal.