Abstract: How does one integrate local information into a global understanding?

 

How does one extract intrinsic structure underlying a data set?

 

How does one capture features of data that are invariant to a wide range of transformations?

 

These are some of the fundamental questions motivating the field of topological data analysis (TDA). In this talk, I will give a gentle introduction to TDA. I will discuss some of my own work on algorithms for TDA addressing problems such as multi-scale data signatures, sensor network coverage testing, and computing meaningful distances between curves and surfaces.

Short Bio: Don Sheehy received his B.S.E. from Princeton University and his Ph.D. in Computer Science from Carnegie Mellon University. He spent two years as a postdoc at Inria Saclay in France. He is now Assistant Professor of Computer Science at The University of Connecticut. His research is in algorithms and data structures in computational geometry and topological data analysis. His 2017 NSF CAREER Award focuses on the challenges of storing, searching, and summarizing complex data sets.