[Colloquium] Reminder - Ashka Shah MS Presentation/Mar 6, 2023

[Megan Woodward]

This is an announcement of Ashka Shah's MS Presentation
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Candidate: Ashka Shah

Date: Monday, March 06, 2023

Time: 10 am CST

Location: JCL 298

Remote Location: https://uchicago.zoom.us/j/99170729126?pwd=ZloyaURRa1dBY0xGSEZiejFOZ2QyZz09<https://urldefense.com/v3/__https://uchicago.zoom.us/j/99170729126?pwd=ZloyaURRa1dBY0xGSEZiejFOZ2QyZz09__;!!BpyFHLRN4TMTrA!5e0Scoji8-fc4qVaY4dEifMyvD4EVPLVcDgi2tfDPqphtl-oYWfFQx8RlTWRXUvzJ_qgFsby9fVbfSrk5fABTyJTrVr7f9Zqn-qJ$>

M.S. Paper Title: Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery

Abstract: Causal discovery of genome-scale networks is important for
identifying pathways from genes to observable traits –e.g. differences in
cell function, disease, drug resistance and others. Causal learners
based on graphical models rely on interventional samples to orient
edges in the network. However, these models have not been shown
to scale up the size of the genome which on the order of 1𝑒3-1𝑒4
genes. We introduce a new learner, SP-GIES, that jointly learns
from interventional and observational datasets and achieves almost
4x speedup against an existing learner for 1,000 node networks.
SP-GIES achieves an AUC-PR score of 0.91 on 1,000 node networks,
and scales up to 2,000 node networks – this is 4x larger than existing
works. We also show how SP-GIES improves downstream optimal
experimental design strategies for selecting interventional experi-
ments to perform on the system. This is an important step forward
in realizing causal discovery at scale via autonomous experimental
design.

Advisors: Rick Stevens

Committee Members: Rick Stevens, Arvind Ramanathan, and Yuxin Chen

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