Jian Cao (曹健)

Jian Cao (曹健)

Assistant Professor in Statistics

University of Houston

About

I am an assistant professor specialized in Computational and Spatial Statistics in the Department of Mathematics at The University of Houston. I worked as a postdoc with Dr. Matthias Katzfuss at Texas A&M University. I obtained my Ph.D. degree in Statistics at King Abdullah University of Science and Technology (KAUST), advised by Dr. Marc Genton. Prior to that, I obtained a B.S. in Mathematics from University of Science and Technology of China.

My research focuses on scalable Gaussian Process (GP) regression, including truncated GP, latent GP, multivariate GP, and high-dimensional GP. During my postdoc, I worked on scalable Gaussian process regression and variable selection, transport maps, and variational Bayes, most of which are based on the Vecchia approximation of Gaussian processes. During my PhD, I studied scalable evaluations of multivariate normal probabilities, mainly exploiting low-rank matrices and efficient quasi-Monte Carlo sampling rules. My research is mostly related to applications in spatial statistics, climate science, and argricultural science.

Download my CV. Contact me: jcao21@central.uh.edu

Interests
  • Gaussian process
  • Truncated normal distribution
  • Scientific computing
  • Spatial statistics
  • Bayesian statistics
Education
  • PhD in Statistics, 2020

    King Abdullah University of Science and Technology

  • B.S. in Mathematics, 2014

    University of Science and Technology of China

Awards and Honors

  • Al-Kindi Statistics Student Research Award, King Abdullah University of Science and Technology, 2020
  • Distinguished Student Paper Award, Section on Statistical Computing and the Section on Statistical Graphics of ASA, 2019

Publications

Published/Accepted

Preprints/Under Review

Teaching

Instructor

  • Instructor for Statistics for Sciences MATH 3339 (Spring 2024)
  • Instructor for Statistics for Sciences MATH 3339 (Fall 2023)

Teaching Assistant

  • Teaching Assistant for MS level Probability and Statistics (Fall 2018)
  • Teaching Assistant for MS level Probability and Statistics (Fall 2017)

Webinar

  • TAMIDS Webinar: Scalable Gaussian Process Approximation and Optimization (April 2022)

Software

  • tlrmvnmvt: an R package for fast computing multivariate normal probabilities. Available on GitHub

  • ExaGeoStat: a parallel high performance unified framework for computational geostatistics on many-core systems. Available on GitHub

Talks & Posters

  • Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. 2023 ICML, Honolulu, HI, USA. Poster
  • Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. 2023 Spatial Statistics, Boulder, CO, USA. Contributed Session
  • Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. 2023 IISA Conference, Golden, CO, USA. Invited Session
  • Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. ASA/IMS SPRING RESEARCH CONFERENCE 2023, Banff, Canada. Contributed Session
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. ENVR 2022 Workshop, Provo, UT, USA. Poster
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. IMSI Gaussian Processes Workshop, Chicago, IL, USA. Poster
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. Joint Statistical Meetings, Washington D.C., USA. Contributed Session
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. ISBA 2022 World Meeting, June 2022, Montreal, Quebec, Canada. Contributed Talk
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. SETCASA Poster Competition, May 2022, College Station, TX, USA. Poster
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. Texas A&M Statistics Cafe, May 2022, College Station, TX, USA. Talk
  • Scalable Gaussian Process Regression and Variable Selection under Automatic Relevance Determination Kernels. TAMIDS Research Conference, Dec 2021, College Station, TX, USA. Talk
  • Sum of Kronecker Products Representation for Spatial Covariance Matrices and Its Factorization. Joint Statistical Meetings, Aug 2020, Virtual Conference. Contributed Session
  • Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student-t Probabilities. Joint Statistical Meetings, Aug 2019, Denver, CO, USA. Topic-Contributed Session
  • Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student-t Probabilities. Big Data Meets Large-Scale Computing, Sep 2018, Los Angeles, CA, USA. Poster
  • Hierarchical-block Conditioning Approximations for High-dimensional Multivariate Normal Probabilities. Joint Statistical Meetings, Aug 2018, Vancouver, BC, Canada. Poster
  • Hierarchical-block Conditioning Approximations for High-dimensional Multivariate Normal Probabilities. Joint Statistical Meetings, Aug 2017, Baltimore, MD, USA. Contributed Session

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