Seminar: Static and Sequential Probabilistic Inverse Problems: An Extreme-Scale Challenge Application By Dr. Kody Law (KAUST)
In many Computational Science and Engineering applications, uncertainties in model, parameters, and initial condition, in concert with increasing computational power, have lead to an increasing interest in probabilistic solutions of large-scale problems. Even crude approximations of the probabilistic solution require many tens of deterministic solves and accurate ones often require thousands or millions.
About
Class schedule: Thursday, May 8th, 2014 from 12:30 pm to 01:30 pm
Location: Building 9, Lecture Hall II, Room 2325
Refreshments: Light lunch available @ 12:00 pm
Brief Biography
Kody Law is a Senior Research Scientist in the SRI Center for Uncertainty Quantification at KAUST. He received his Ph.D. in Mathematics from the University of Massachusetts in 2010 and subsequently held a position as a postdoctoral research fellow at the Mathematics Institute of Warwick University until he joined KAUST in 2013. During his short academic career, he has given more than 50 invited lectures around the world and published 37 peer-reviewed journal articles and book chapters in the areas of computational applied mathematics, physics, and dynamical systems. His current research interests are focused on inverse uncertainty quantification in high dimensions, i.e. on large(target extreme)-scale: data assimilation, filtering, and probabilistic inverse problems. In particular, a recently developing interest is in algorithmic adaptation to emerging architecture, in the context of forwarding and inverse uncertainty quantification applications.
Related People
Related Researchers
Kody J. H. Law
- Senior Research Scientist, Stochastic Numerics Research Group