## Scalable Gaussian Process Analysis and Nonparametric Learning

- April 18, 2016
- 1:15 p.m.
- LeConte 312

## Abstract

Gaussian processes are the cornerstone of statistical analysis. They are widely used in various areas including scientific computing and machine learning. Example applications are quantifying the simulation uncertainty caused by stochastic inputs, designing effective computer experiments in a vast parameter space, and recognizing patterns in speech, image, and text data. Theoretically grounded, Gaussian processes incur a significant challenge in computations, because the numerical linear algebra costs are generally cubic in the data size. In this talk, I present several lines of work that addresses the challenge facing large-scale data, for computations such as sampling, prediction, and parameter estimation. These efforts hint on a quest for unifying treatments of kernel matrices that are fully dense but structured. I will conclude the talk by presenting an ongoing approach that establishes a linear-complexity framework for computing such matrices, including the high dimensional setting that is currently a major hurdle.

**Bio:**

Jie Chen is a Research Staff Member at the IBM Thomas J. Watson Research Center. Centrally themed at matrices, his interests span a variety of areas, including numerical linear algebra, scientific computing, parallel processing, applied statistics, machine learning, and artificial intelligence. His work is published in various leading journals in applied mathematics and computer science, including a paper awarded the Student Paper Prize by the Society for Industrial and Applied Mathematics. Jie received his undergraduate degree in Mathematics at Zhejiang University and Ph.D. in Computer Science at the University of Minnesota. He worked at Argonne National Laboratory before joining IBM. More information can be found in his personal homepage http://jie-chen-ibm.appspot.com/ .