Time: 3:45pm, Thursday, October 7, 2021
Location: Zoom link: https://smu.zoom.us/j/5120043096?pwd=TmR4NE8yUzdLbmtyYzlUellGSjJ4dz09
Passcode: caimeeting, zoom meeting id 512 004 3096
Title: Revisiting the Classical Least-Squares Formulation for Computational Learning and Inversion
Speaker: Kui Ren, Department of Applied Physics & Applied Mathematics, Columbia University
Abstract: The classical least-squares formulation has provided a successful framework for the computational solutions of learning and inverse problems. In recent years, modifications and alternatives have been proposed to overcome some of the disadvantages of this classical formulation in dealing with new applications. We will present some recent progress on the understanding of a general weighted least-squares optimization framework in such a setting. The main part of the talk is based on joint works with Bjorn Engquist and Yunan Yang.
Biography:Kui Ren received his PhD in applied mathematics in 2006 from Columbia University. He then spent a year at the University of Chicago as a L. E. Dickson instructor before moving to University of Texas at Austin to become an assistant professor in mathematics in 2008. He returned to Columbia University in 2018 as a professor in applied mathematics. Kui Ren’s recent research interests include inverse problems, mathematical imaging, random graphs, fast algorithms, kinetic modeling, and computational learning.