Hi, I'm Ashka Shah. I'm a fifth-year Ph.D. student at the University
of Chicago, advised by Rick Stevens.
I am interested in accelerating discovery for high-dimensional problems in science with AI and autonomous experimentation. My recent
work is on causal discovery with graph partitioning for recovering causal links in genome-scale networks.
I did my undergraduate degree at Harvey Mudd College, majoring in physics. After that, I worked at the
National Ignition Facility where I wrote code for
simulating laser physics.
Last summer, I interned at the Flatiron Institute in Olga Troyanksaya's lab working on causal discovery
of human tissue-specific gene regulatory networks. Prior to that I interned at Argonne National Laboratory working in the Rapid Prototyping Lab for autonomous experimentation.
Publications
- Causal Discovery over High-Dimensional Structured Hypothesis Spaces with
Causal Graph Partitioning
Ashka Shah, Adela DePavia, Nathaniel Hudson, Ian Foster, Rick Stevens
- Causal Discovery and Optimal Experimental Design for
Genome-Scale Biological Network Recovery
Ashka Shah, Arvind Ramanathan, Valerie Hayot-Sasson, Rick Stevens
- Probing Decision Boundaries in Cancer Data Using
Noise Injection and Counterfactual Analysis
Rajeev Jain, Ashka Shah, Jamaludin Mohd-Yusof, Justin M. Wozniak, Thomas S. Brettin, Fangfang Xia, Rick Stevens
- Scaffold-Induced Molecular Graph (SIMG):
Effective Graph Sampling Methods for
High-Throughput Computational Drug Discovery
Austin Clyde, Ashka Shah, Max Zvyagin, Arvind Ramanathan, Rick Stevens
-
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
(full list of authors in link)
Ashka Shah
shahashka[at]uchicago.edu
PhD Candidate in Computer Science
Department of Computer Science
University of Chicago