Data Science & AI Lecture Series
Extracting, Visualizing, and Analyzing Topological Features with Discrete Representations
Josh Levine
Extracting, Visualizing, and Analyzing Topological Features with Discrete Representations
| When | Thursday, March 5, 2026, 10:30 AM – 11:30 AM (MT) |
|---|---|
| Where | Evans Conference Room (WEB 3780) |
Abstract
Topological features provide multi-scale summaries of the behavior of continuous data from diverse applications ranging from astrophysics to medicine. Nevertheless, computing them robustly is challenging due to numerical precision issues. A promising strategy is to first convert the input to a discrete representation that satisfies criteria introduced by Forman’s discrete Morse theory. While numerous approaches exist to discretize the restricted case of gradient fields from scalar data, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. In this talk, I will present recent work that uses local evaluation to create discrete vector fields in linear time from two-dimensional, triangulated vector fields. I will also frame this work within my contributions to the Topological ToolKit (TTK), an open source software platform for topological data analysis, led by collaborators at UPMC Sorbonne.
Speaker
Josh Levine
Joshua A. Levine is an associate professor in the Department of Computer Science at University of Arizona. Prior to starting at Arizona in 2016, he was an assistant professor at Clemson University from 2012 to 2016, and he is a postdoctoral alumnus of the University of Utah’s SCI Institute, 2009 to 2012. He is a recipient of the 2018 DOE Early Career award. He received his PhD in Computer Science from The Ohio State University in 2009 after completing BS degrees in Computer Engineering and Mathematics in 2003 and an MS in Computer Science in 2004 from Case Western Reserve University. His research and teaching interests include visualization, topological analysis, geometric modeling, and computer graphics.
Tags: visualization
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