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 a PhD student in the Department of Computer Science at Johns Hopkins University, advised by Ilya Shpitser. In July 2021, I will start as an Assistant Professor of Computer Science at Williams College.

The primary focus of my research is methods development for causal inference. I also study the application of causal inference and machine learning in computational genomics for improving patient outcomes.

Email: First letter of my first name concatenated with my last name at jhu.edu.

Twitter: r0ntu

News:

March 5, 2021: The paper on differentiable causal discovery in the presence of unmeasured confounders has been accepted at AISTATS 2021. The camera-ready version of the paper is available here and Python code for the method is available here.
Jan 22, 2021: I accepted a tenure-track positition as Assistant Professor in the Department of Computer Science at Williams College!
October 15, 2020: New draft on differentiable causal discovery in the presence of unmeasured confounders is now up on arXiv at this link.
May 1, 2020: Super happy to announce the 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.