About
I am a postdoctoral researcher in the Department of Statistics and the Institute of Data Science at Texas A&M University, supervised by Dr. Matthias Katzfuss. 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.
I have broad research interests in scalable computational methods and theories behind those. Currently in my postdoc, I am working 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. My goal is to make scalable Gaussian-based methods a ready-to-use tool for various machine-learning tasks.
Download my CV. Contact me: jian.cao@tamu.edu
- Gaussian processes
- Machine learning
- Scalable statistical computing
- Spatial statistics
- Dimension reduction
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PhD in Statistics, 2020
King Abdullah University of Science and Technology
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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
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Cao, J., Guinness, J., Genton, M. G., & Katzfuss, M. (2022) Scalable Gaussian-process Regression and Variable Selection using Vecchia Approximations. Journal of Machine Learning Research 23(348), pp.1-30
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Cao, J., Durante, D., & Genton, M. G. (2022). Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors. Journal of Computational and Graphical Statistics 31(3), pp.709-720
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Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. (2022). tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-rank Methods in R. Journal of Statistical Software 101, pp.1-25
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Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. (2021). Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student-t Probabilities. Statistics and Computing, 31(1), pp.1-16
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Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. (2021). Sum of Kronecker Products Representation and Its Cholesky Factorization for Spatial Covariance Matrices from Large Grids. Computational Statistics & Data Analysis, 157, pp.107165
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Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. (2019). Hierarchical-block Conditioning Approximations for High-dimensional Multivariate Normal Probabilities. Statistics and Computing 29, pp.585-598
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Huang, J., Fang, F., Turkiyyah, G., Cao, J., Genton, M. G., & Keyes, D. E. (2021). An O(N) Algorithm for Computing Expectation of N-dimensional Truncated Multivariate Normal Distribution I: Fundamentals. Advances in Computational Mathematics 47(5), pp. 1-34
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Abdulah, S., Li, Y., Cao, J., Ltaief, H., Keyes, D. E., Genton, M. G., & Sun, Y. (2022). Large-scale Environmental Data Science with ExaGeoStatR. accepted by Environmetrics
Preprints/Under Review
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Cao, J., Zhang, J., Sun, Z., & Katzfuss, M. (2022) Locally Anisotropic Covariance Functions on the Sphere. submitted
Teaching
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)
Talks & Posters
- 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
Contact
- jian.cao@tamu.edu
- 155 Ireland St, College Station, TX 77840