CSE Distinguished Seminar | Andrew Stuart

Abstract

Many inverse problems arising in applications may be cast as optimization or sampling problems in which the parameter-to-data map is provided as a black-box, derivatives may not be readily available and the evaluation of the map itself may be subject to noise. I will describe the derivation of mean-field (stochastic) dynamical systems which address such problems and show how particle approximations lead to derivative-free algorithms. I will overview some of the analysis of the resulting methods, link the work to parallel developments in consensus-based optimization, and describe open problems.  The work will be illustrated throughout by examples from the physical sciences.

Bio

Andrew Stuart has research interests in applied and computational mathematics, and is interested in particular in the question of how to optimally combine complex mechanistic models with data. He joined Caltech in 2016 as Bren Professor of Computing and Mathematical Sciences, after 17 years as Professor of Mathematics at the University of Warwick (1999–2016). Prior to that he was on the faculty in The Departments of Computer Science and Mechanical Engineering at Stanford University (1992–1999), and in the Mathematics Department at Bath University (1989–1992). He obtained his PhD from the Oxford University Computing Laboratory in 1986, and held postdoctoral positions in Mathematics at Oxford University and at MIT in the period 1986–1989