January 26, 2023

About the speaker

Dr. Mike Lindstrom is an Assistant Professor at UTRGV's SMSS School of Mathematical And Statistical Sciences as of Fall 2022. He completed his undergrad (BSc. Physics and Mathematics combined honours) and grad studies (MSc and PhD in Applied Math) at University of British Columbia, Vancouver, BC, Canada. He conducted postdoctoral work as an Assistant Adjunct Professor for the Program in Computing at UCLA (University of California, Los Angeles). His research areas include mathematical modelling, applied differential equations, scientific computing, formal asymptotics, and data science. Currently, he is Faculty Advisor for The Mathematical Contest in Modeling (MCM) and he has initiated an informal Ultimate Frisbee group at UTRGV's Edinburg campus. Anyone in the Math & Stats department on either campus is welcome: grad students, postdocs, staff, and faculty, plus friends.

Anomalies and Commonalities

This talk will cover two research projects. In the first, we seek to identify contextual anomalies among time series with possible missing data. By generalizing Kernel Density Estimation to Hilbert Spaces, we develop tools to identify anomalous time series, test them against competing methods on synthetic data, and then employ the tools to identify anomalous event records among airplane fleets. In the second project, we combine topic modelling through Nonnegative Matrix Factorization and regression on a continuous response variable. Previous authors have covered the case of topic modelling for classification; here, we show the idea can be extended to regression, applying it to Rate My Professor reviews and predicting an instructor's rating from student comments. We identify interpretable groups of words (topics) such that their level of representation in a review has a quantifiable effect on the associated rating.