shahashka.github.io/index.html at master · shahashka/shahashka.github.io · GitHub

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  <title>Ashka Shah</title>
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				</p><h4 style="font-size: 17.5px;"> Hi, I'm Ashka Shah. I'm a PhD candidate at the University
            of Chicago, advised by <a href="https://cs.uchicago.edu/people/rick-stevens/">Rick Stevens</a> (expected graduation June 2026).
            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.<br>
					<br>
					I did my undergraduate degree at Harvey Mudd College, majoring in physics. After that, I worked at the
            <a href="https://lasers.llnl.gov/">National Ignition Facility</a> where I wrote code for
            <a href="https://computing.llnl.gov/projects/virtual-beamline-code">simulating laser physics.</a> <br>
            Last summer, I interned at the Flatiron Institute in <a href="https://function.princeton.edu/">Olga Troyanksaya's lab</a> working on causal discovery
            of human tissue-specific gene regulatory networks. Prior to that I interned at Argonne National Laboratory working in the <a href="https://rpl.cels.anl.gov/">Rapid Prototyping Lab</a> for autonomous experimentation.
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                <h2 id="publications">Publications</h2>
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                  <li class="paper" words="causal discovery, graph partitioning, biological networks"><a href="https://arxiv.org/abs/2406.06348">Causal Discovery over High-Dimensional Structured Hypothesis Spaces with
                    Causal Graph Partitioning</a> <br> <b>Ashka Shah</b>, Adela DePavia, Nathaniel Hudson, Ian Foster, Rick Stevens. <br> TMLR 2025, ICML AI for Science Workshop 2024 (Best Paper) </li>
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                <li class="paper" words="causality, structure learning, optimal experimental design"><a href="https://dl.acm.org/doi/abs/10.1145/3592979.3593400">Causal Discovery and Optimal Experimental Design for
                    Genome-Scale Biological Network Recovery </a> <br> <b>Ashka Shah</b>, Arvind Ramanathan, Valerie Hayot-Sasson, Rick Stevens. <br> PASC 2023</li>
                </ul>

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                <li class="paper" words="cancer, deep learning, interpretability"><a href="https://web.cels.anl.gov/~woz/papers/Counterfactuals_2021.pdf">Probing Decision Boundaries in Cancer Data Using
Noise Injection and Counterfactual Analysis </a> <br> Rajeev Jain, <b>Ashka Shah</b>,  Jamaludin Mohd-Yusof, Justin M. Wozniak,  Thomas S. Brettin, Fangfang Xia, Rick Stevens</li>
                </ul>

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                <li class="paper" words="drug discovery, virtual screening, chemical space"><a href="https://arxiv.org/abs/2109.05012">Scaffold-Induced Molecular Graph (SIMG):
                    Effective Graph Sampling Methods for <br> High-Throughput Computational Drug Discovery </a>   <br>Austin Clyde, <b>Ashka Shah</b>, Max Zvyagin, Arvind Ramanathan, Rick Stevens</li>
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                <li class="paper" words="drug discovery, virtual screening, workflows, deep learning"><a href="https://dl.acm.org/doi/10.1145/3472456.3473524">
                    IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads </a> <br> (full list of authors in link)</li>
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                <p><div style="font-family: 'Oswald', sans-serif; font-size: 32px;"><b>Ashka Shah</b></div><br>
                <b>shahashka[at]uchicago.edu</b><br>
                PhD Candidate in Computer Science<br>
                Department of Computer Science<br>
                University of Chicago<br>
                <dd><a href="https://github.com/shahashka">Github</a></dd>
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