معرفی
Jian Cao is an Assistant Professor in the Department of Mathematics at the University of Houston, specializing in Computational and Spatial Statistics. He earned his Ph.D. in Statistics from KAUST under Dr. Marc Genton and completed postdoctoral work at Texas A&M University under Dr. Matthias Katzfuss. His research focuses on scalable Gaussian Process (GP) regression methods, including Vecchia approximations, truncated multivariate normal distributions, and applications in climate and agricultural science.
Education includes a B.S. in Mathematics from University of Science and Technology of China (2014), a Master in Finance from Shanghai Jiaotong University (2016), and a Ph.D. in Statistics from KAUST (2020).
Research interests emphasize computational efficiency in statistical modeling, with contributions to scalable algorithms for high-dimensional data. Key themes include Gaussian processes, multivariate normal probabilities, and low-rank matrix methods. His work bridges theory and application, addressing challenges in environmental and agricultural data science.
Notable awards include the Al-Kindi Statistics Student Research Award (2020) and Best Student Paper (2019). His publications span journals like the Journal of the American Statistical Association and conferences such as ICML, focusing on scalable statistical methods and software development (e.g., tlrmvnmvt package).
Teaching responsibilities include courses on statistics for the sciences and inferential statistics at the University of Houston. His software contributions prioritize high-performance computing solutions for large-scale geospatial and statistical problems.
