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.
I have broad research interests in scalable computational methods for statistical machine learning. 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
There is an opening for a postdoc position with a research focus in high-dimensional Gaussian process regression and the intersection between Gaussian process and machine-learning. Please contact me if you are interested.
PhD in Statistics, 2020
King Abdullah University of Science and Technology
B.S. in Mathematics, 2014
University of Science and Technology of China
Zhang, X., Abdulah, S., Cao, J., Ltaief, H., Sun, Y., Genton, M. G., & Keyes, D. E. (2024). Parallel Approximations for Multivariate Normal Probability Computation in Confidence Region Detection Applications. accepted by IEEE International Parallel and Distributed Processing Symposium
Cao, J., Zhang, J., Sun, Z., & Katzfuss, M. (2023). Locally Anisotropic Covariance Functions on the Sphere. accepted by Journal of Agricultural, Biological and Environmental Statistics
Cao, J., Kang, M., Jimenez, F., Sang, H., Schäfer, F., & Katzfuss, M. (2023). Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. accepted by the 40th International Conference on Machine Learning
Abdulah, S., Li, Y., Cao, J., Ltaief, H., Keyes, D. E., Genton, M. G., & Sun, Y. (2023). Large-scale Environmental Data Science with ExaGeoStatR. accepted by Environmetrics
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
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
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
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
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
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
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
Cao, J., & Katzfuss, M. (2023). Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities. submitted