Abstract: Numerical linear algebra, the study of how to do linear algebra on computers, has been a core topic in computing since the outset. NLA is at the core of many of today's most compute intensive tasks, including machine learning and scientific computing. Over the past 20 years, randomization has emerged as a powerful technique for designing NLA algorithms, and randomized algorithms now provide the best practical and theoretical performance for many basic linear algebra tasks, such as low-rank approximation and least squares regression. This talk will provide an introduction to the field, and introduce several emerging directions that I am interested in.
Bio: Tyler Chen is an Assistant Professor of Computer Science at NYU Shanghai. His research focuses on the design and analysis of fast and theoretically justified (randomized) algorithms for core linear algebra tasks, with the overarching goal of developing tools to support the advancement of knowledge in current scientific applications. He previously worked as an Applied Research Lead at JPMorganChase and as a Courant Instructor at New York University (sponsored by Chris Musco). He received his PhD in Applied Math from the University of Washington, where he was advised by Anne Greenbaum and Tom Trogdon. Even earlier, he did his undergraduate studies at Tufts University, where he studied math, physics, and studio art.