- Speaker
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Per-Gunnar Martinsson, Ph.D.Professor of Mathematics, University of Texas at Austin
Presidential Lectures are a series of free public colloquia spotlighting groundbreaking research across four themes: neuroscience and autism science, physics, biology, and mathematics and computer science. These curated, high-level scientific talks feature leading scientists and mathematicians and are designed to foster discussion and drive discovery within the New York City research community. We invite those interested in these topics to join us for this weekly lecture series.
At the core of many computational simulations of physical systems lies a linear algebraic problem, such as a system of linear equations to be solved or an eigenvalue problem. Similar problems also arise throughout computational statistics and data science. Because such computations are often the most computationally expensive part of a simulation, they frequently determine both the simulation’s feasibility and the level of physical detail it can achieve.
In this Presidential Lecture, Per-Gunnar Martinsson will describe how ideas from random matrix theory and high-dimensional probability have, in recent years, enabled a new generation of algorithms that are far faster than traditional deterministic methods and have enabled simulations of problems once considered intractable. One of the most important features of these randomized methods is that they require very little data movement, making them especially well-suited to modern computing hardware. The lecture will present methods for low-rank approximation, for solving linear systems, and for constructing highly accurate compressed representations of continuum operators, including solution operators for elliptic partial differential equations, Dirichlet-to-Neumann maps and time-evolution operators.
About the Speaker
Martinsson is a professor of mathematics and the W. A. “Tex” Moncrief, Jr. Endowed Chair in Simulation-Based Engineering and Sciences at the University of Texas at Austin, as well as deputy director of the university’s Oden Institute for Computational Engineering and Sciences. He earned a Ph.D. in computational and applied mathematics from UT Austin in 2002 after receiving degrees in engineering physics and mathematics from Chalmers University of Technology in Sweden. His research focuses on the development of fast algorithms for scientific computing and data science, including randomized linear algebra, fast solvers for partial differential equations and structured matrix computations. He is a Society for Industrial and Applied Mathematics Fellow and a 2017 Germund Dahlquist Prize recipient.