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Subspace Acceleration for Large-Scale Bayesian Inverse Problems
Date:2018/6/26     Browse:93

Speaker:     Tiangang Cui. Monash University

Time:          10:15—11:15, June 26  

Location:    Room 1A-200, SIST Building

Host:          Prof. Qifeng Liao

Abstract:

Algorithmic scalability to high-dimensional models is one of the central challenges in solving large-scale Bayesian inverse problems.

By exploiting the interaction among various information sources and model structures, we will present a set of certified dimension reduction methods for identifying the intrinsic dimensionality of inverse problems. The resulting reduced dimensional subspaces offer new insights into the acceleration of classical Bayesian inference algorithms for solving inverse problems. We will discuss some old and new algorithms that can be significantly accelerated by the reduced dimensional subspaces.

Bio: 

Tiangang Cui is a Lecturer in the School of Mathematical Sciences at Monash University. He has previously held positions at the Massachusetts Institute of Technology and the ExxonMobil Corporation.

He has been worked on a wide range of topics on the intersection of data analytics and computational mathematics. His research interests include Bayesian inference, inverse problems, model reduction, stochastic computation, and statistical learning.

SIST-Seminar 18052