Mere data makes a man —
A and C
and T and G.
The alphabet of you —
I am only two — 1 and
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
- Mar 5, 2021: The paper on differentiable causal
discovery in the presence of unmeasured confounders
has been accepted at AISTATS 2021 -- camera-ready
- 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
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
- Jan 15, 2020: Razieh and my
introductory course on Causal Inference was
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,