Host: Prof. Nina Amenta
Abstract: In recent years, considerable progress has been made in analyzing data for inferring the topology of a space from which the data is sampled. Current popular approaches often face two major problems. One concerns with the size of the complex that needs to be built on top of the data points for topological analysis; the other involves selecting the correct parameter to build them. In this talk, I will describe some recent progress we made to address these two issues in the context of inferring homology from sample points of a smooth manifold sitting in an Euclidean space. I will describe how we sparsify the input point set and to build a complex for homology inference on top of the sparsified data, without requiring any user supplied parameter. Our sparsification algorithm guarantees that the data is sparsified at least to the level as specified by the so-called local feature size; and at the same time, the sparsified data is adaptive as well as locally uniform. This is joint work with T. K. Dey and F. Fan.
Bio: Yusu Wang is Associate Professor of Computer Science and Engineering Department at the Ohio State University. She obtained her PhD degree from Duke University, where she received the Best PhD Dissertation Award at CS Dept., Duke U, in 2004. Before joining OSU in 2005, she was a post-doctoral fellow at Stanford University from 2004–2005. Yusu Wang works in the fields of Computational geometry and Computational topology. She is primarily interested in developing effective and theoretically justified algorithms for data / shape analysis using geometric and topological ideas and methods, and in applying them to practical domains, including computational biology, computer graphics and visualization. She received DOE Early Career Principal Investigator Award in 2006, and NSF Career Award in 2008. She is an associate editor of Journal of Computational Geometry.
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