What is the shape of your data? An introduction to computational topology and its application in the data sciences
Computational topology is a relatively young field in algebraic topology. Tools from computational topology, in particular persistent homology, have proven successful in many scientific disciplines. Persistence diagrams, a typical way to study persistent homology, contain fruitful information about the underlying objects. Extracting features from persistence diagrams is one of the major research areas in this field. In this talk, we will give a brief introduction to persistent homology, and we will demonstrate methods we propose to summarize persistence diagrams. Applications to various datasets from cell biology, medical imaging, physiology, and climatology, will be presented to illustrate the methods.Download Slides
Assistant Professor, UNC Greensboro on March 1, 2019 at 11:45 AM in EB2 1230.
Yu-Min Chung received his Ph.D. in Mathematics from Indiana University Bloomington in 2013 in the field of computational dynamics under the supervision of Professor Michael Jolly. He worked as an Adams Assistant Professor at the University of Kansas during 2013-2014 , and was a postdoctoral researcher at the College of William and Mary during 2014-2017. Currently, he is an Assistant Professor of Mathematics at the University of North Carolina at Greensboro. He also held two short term visiting positions at Federal University of Rio de Janeiro in Brazil in 2013, and National Center for Theoretical Sciences in Taiwan in 2018. His main research interest is Topological Data Analysis (TDA). He has collaborated with scientists from different disciplines to uncover patterns and structure in various datasets which include medical imaging, biological signals, and climatology.
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