Please note: This AIcES talk has a location change.
We will meet in Innovation South 2108AB, not in W307.

Speaker: Baris Coskunuzer, University of Texas at Dallas

Title: Topological Machine Learning: From Drug Discovery to Cancer Detection

Abstract: Topological machine learning applies ideas from topology to extract stable, multiscale summaries of structure in complex data. I will introduce its core tools through intuition and simple visuals, then highlight two applications. First, in computer-aided drug discovery, I will show how multiparameter persistence yields informative graph representations of molecules for property prediction and virtual screening. Second, in cancer detection from histopathology, I will show how cubical persistence captures tissue micro-architecture directly from images. Across five cancer types, our models match or surpass strong deep-learning baselines while providing robustness and interpretability. The talk is self-contained and aimed at advanced undergraduates in mathematics, science, and engineering. No prior background in topology or machine learning is assumed.

Short bio: Baris Coskunuzer is a Professor of Mathematics at the University of Texas at Dallas, where he leads the Topological Machine Learning Group. His research bridges geometry, topology, and artificial intelligence, with recent work focusing on topological methods for graph learning and medical image analysis. He has published extensively in both pure and applied mathematics, with research supported by the National Science Foundation, NASA, and the Simons Foundation. He is deeply committed to mentoring diverse trainees and fostering interdisciplinary collaborations at the interface of topology and data science.

There is a registration form here to help the organizers plan

Refreshment is available at 2:00PM

 AIcES website: https://aic-es.github.io/AIcES-seminar/