Mere data makes a man —
A and C
and T and G.
The alphabet of you —
I am only two — 1 and
I am a PhD student in the Department of Computer Science
at Johns Hopkins University, advised
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.
- 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
and Python code for the method is
- 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
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,