MSSE Industrial Seminar Series: Faculty Research
We will have two talks presented by faculty members from the Computer Science and Engineering Department.
Talk 1: "Getting more signal with less data" by Prof. Dan Knights
Speaker Bio: Dr. Dan Knights is a data scientist and puzzle enthusiast who does computational biology. He is an Associate Professor in the Biotechnology Institute and the Department of Computer Science and Engineering at the University of Minnesota. Dan received his B.A. from Middlebury College, and his Ph.D. from the University of Colorado, both in Computer Science, followed by completing a post-doctoral fellowship at Harvard. His research uses data mining and machine learning to better understand the natural world, including studying the microbiome in human disease. In 2015, Dan was named a McKnight Land-Grant Professor by the University of Minnesota, and he received the 2019 Innovation Award from the University of Minnesota’s Office of the Vice President for Research together with Kenny Beckman and Daryl Gohl. From 2016-2020, Dan was co-founder and CEO of CoreBiome, a genomics company that has merged with Diversigen.
Talk 2: "A pictorial overview of common machine learning methods" by Prof. Daniel Boley
We give a quick tour of many data-driven machine learning methods currently in common use by using graphical illustrations on small model problems. Methods include both classical methods and more modern deep learning methods.
Speaker Bio: Daniel Boley received his Ph.D. degree in Computer Science from Stanford University in 1981. Since then, he has been on the faculty of the Department of Computer Science and Engineering at the University of Minnesota, where he is now a full professor. Dr. Boley is known for his past work on numerical linear algebra methods for control problems, parallel algorithms, iterative methods for matrix eigenproblems, inverse problems in linear algebra, as well as his more recent work on computational methods in statistical machine learning, data mining, and bioinformatics. His current interests include scalable algorithms for convex optimization in machine learning, the analysis of networks and graphs such as those rising from metabolic biochemical networks and networks of wireless devices. He was an associate editor for the SIAM Journal of Matrix Analysis and has chaired several technical symposia at major conferences. He is a senior member of the IEEE and a distinguished scientist of the ACM.