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Title: The underlying topology of data

Abstract: Topology, and particularly algebraic topology, seeks to develop computable invariants to quantify the shape of abstract spaces. This talk will be about how such invariants can be used to analyze scientific data sets, in tasks like time series analysis, semi-supervised learning and dimensionality reduction. I will use several examples to illustrate real applications of these ideas.

Bio: Jose Perea holds a Ph.D. in Mathematics from Stanford University (2011) and a B.Sc. in Mathematics from Universidad del Valle (Summa cum laude and Valedictorian, 2016). He was a visiting assistant professor in the department of Mathematics at Duke University from 2011 to 2015, and a member of the Institute for Mathematics and its Applications (IMA) at the University of Minnesota during the Fall of 2014. In August of 2015 he joined Michigan State University as an Assistant Professor with joint appointments in the department of Computational Mathematics, Science & Engineering (CMSE), and the department of Mathematics. He is the recipient of a 2020 NSF CAREER award,  a 2020 honoree of Lathisms during Hispanic heritage month, a 2018 honoree of Mathematically Gifted and Black during black history month, and recognized as being on the top 5% of teachers at Duke University  (2013).

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