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Yusu Wang: Topological and Geometric Methods for Graph AnalysisWhen: Thursday, April 4 | 2 – 3:30 pm
Where: Computer Science and Engineering Building, Room, 1202
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, I will show how topological and geometric ideas can be used to analyze graph data, which occurs ubiquitously across science and engineering. Graphs could be geometric in nature, such as road networks in GIS, or relational and abstract, such as protein-protein interaction networks. I will particularly focus on the reconstruction of hidden geometric graphs from noisy data, as well as graph matching and classification. I will discuss the motivating applications, algorithm development, and theoretical guarantees for these methods. Through these topics, I aim to illustrate the important role that geometric and topological ideas can play in data analysis.
Short bio: Wang is a professor of Computer Science and Engineering Department at Ohio State University, and Faculty co-Lead for the Foundations CoP at Translational Data Analytics Institute at OSU. She obtained her Ph.D. from Duke University, where she received the Best Ph.D. Dissertation Award at the Computer Science Department in 2004. Before joining OSU, she was a postdoctoral fellow at Stanford University. She primarily works in the fields of computational geometry, and computational and applied topology. Her work lies at the intersection of computer science (especially algorithms) and applied mathematics (especially applied topology, discrete and combinatorial geometry).