Abstract: In this talk, I will present methods of topological data analysis engaged with statistics for solving real data problems. As a paradigm, I will discuss supervised learning, and a new classification approach will be presented using a novel Bayesian framework for persistent homology. An application to materials will be discussed.
Speaker’s Bio: Vasileios Maroulas is a Professor of Mathematics with joint appointments at the Business Analytics and Statistics, and the Bredesen Center at the University of Tennessee. He is a Senior Research Fellow at the US Army Research Lab, an Elected Member of the International Statistical Institute and a co-Editor-in-Chief of Foundations of Data Science published by the American Institute of Mathematical Sciences. He was a Lockheed Martin Postdoctoral Fellow at the IMA at the University of Minnesota, and he joined the University of Tennessee as an Assistant Professor in 2010. Maroulas was also a Leverhulme Trust Fellow at the University of Bath, UK during 2013-2014. His research interests span from computational statistics to applied probability and computational topology and geometry with applications in data analysis and quantum computing. His research has been funded by AFOSR, ARL, ARO, DOE, NSF, the Simons Foundation, and the Leverhulme Trust in the UK.
Last Updated: November 11, 2020 - 11:36 am