Discrete Morse-based Graph Skeletonization and Data Analysis
by Yusu Wang

Abstract:
In recent years, topological and geometric data analysis (TGDA) has emerged as a new and promising field for processing, analyzing and understanding complex data. Indeed, geometry and topology form natural platforms for data analysis, with geometry describing the ”shape” and ”structure” behind data; and topology characterizing / summarizing both the domain where data are sampled from, as well as functions and maps associated to them. In this talk, Yusu will show how the topological objects and ideas can be combined with algorithmic developments to lead to new approaches for inferring hidden graph skeleton structure behind (low and high dimensional) data; as well as how they can be combined with machine learning pipelines for further data analysis tasks (e.g., to neuroscience and to material science). This talk is based on multiple projects with multiple collaborators and references will be given during the talk.

Bio:
Yusu Wang is currently a Professor in the Halicioglu Data Science Institute at University of California, San Diego. Prior to joining UCSD, she was a Professor in the Computer Science and Engineering Department at the Ohio State University, where she also co-directed the Foundation of Data Science Research CoP at Translational Data Analytics Institute (TDAI)@OSU from 2018-2020. She obtained her PhD degree from Duke University in 2004, where she received the Best PhD Dissertation Award at the Computer Science Department. From 2004-2005, she was a post-doctoral fellow at Stanford University. Yusu Wang primarily works in the fields of geometric and topological data analysis. She is particularly interested in developing effective and theoretically justified algorithms for data analysis using geometric and topological ideas and methods, as well as in applying them to practical domains. She received DOE Early Career Principal Investigator Award in 2006, and NSF Career Award in 2008. Her work received several best paper awards. She is on the editorial boards for SIAM Journal on Computing (SICOMP) and Journal of Computational Geometry (JoCG). She is currently a member of the Computational Geometry Steering Committee. She also serves in SIGACT CATCS (Committee for the Advancement of Theoretical Computer Science) and AWM Meetings Committee.

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Agenda (Pacific Daylight Time, UTC -07)

• 5:30 - 5:40 pm -- Gathering and introductions
• 5:40 - 6:30 pm -- Talk
• 6:30 - 7:00 pm -- Q & A, discussion

Links to slides and videos of meetup presentations are available on the SDML GitHub repo https://github.com/SanDiegoMachineLearning/talks

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