Speaker: Dr. Shengxin Zhu (朱圣鑫博士)
Time: 12:30-13:30, 2 November 2021 (Tuesday) (Beijing time)
Venue: C404, Lijiao Building, BNU at Zhuhai
Abstract
Log determinants of covariance matrices play an essential role in high dimensional Gaussian process, Baysian inference, and uncertainty quantification. It is necessary to calculate several inference criteria including the log likelihood; and the log determinants can also be required to be computed in indeterminate steps in the maximum likelihood estimation process. A fast and scalable log determinant solver is essential to make the maximum likelihood based methods feasible for high dimensional problems with many parameters. In this talk we shall introduce this problem, access several methods and discuss possible research directions.
About Dr. Zhu
Shengxin Zhu received his B.S. in Information and Computational Science from Xiamen University, MSc in Computational Mathematics from China Academy of Engineering Physics and his Ph.D. in Mathematics from Oxford. Shengxin was a winner of the Oxford Center for Collaborative and Applied Mathematics studentship for international students. He wrote his thesis Numerical Linear Algebra Approximation Involving Radial Basis Functions under the direction of Prof. Andy Wathen. During his Ph.D. study, he once worked as a knowledge transfer associate for VSN international Ltd, this is a position funded by EPSRC, VSNi, and Oxford. His master thesis was on high-performance computing for distributed supercomputers in China.
And Before joining UIC in 2020, he held positions at Xi’an Jiaotong-Liverpool University. Dr. Shengxin Zhu joined the BNU-UIC joint research center for mathematics in 2020. He holds a dual associated professorship in data science and applied mathematics.
Dr. Shengxin Zhu is an experienced teacher and supervisor. He has taught courses in Calculus I, Calculus II, Linear Algebra for Mathematics Students, Linear Algebra for Engineering Students, Discrete Mathematics, Numerical Analysis, Optimization Theory, and Recommendation System.
