Pulong Ma

Assistant Professor
Department of Statistics, Iowa State University

3417 Snedecor Hall, 2438 Osborn Dr, Ames, IA 50011
plma [at] iastate [dot] edu

Full CV (pdf) · GitHub · Google Scholar · ORCID

My research develops Bayesian methods and theory at the intersection of spatial statistics and machine learning for understanding complex space-time processes and physical systems. My current statistical work is mainly focused on spatial generative modeling, Bayesian tree methods, and space-time covariance modeling involving tools such as Gaussian process and point process. These methods have been used to address real-world problems in remote sensing, climate science, natural hazards, engineering, and physical sciences.

Funding

• DOE Genesis Mission DE-SC0026595, “A Spatial Generative Bayesian Computation Framework for Inferring Turbulence–Microphysics Interactions.” Lead PI, 2026–2027.
• NSF DMS-2348154, “Modeling Multivariate and Space-Time Processes: Foundations and Innovations.” Sole PI, 2023–2026.
• NSF DMS-2348163, “Collaborative Research: Bayesian Residual Learning and Random Recursive Partitioning Methods for Gaussian Process Modeling.” PI, 2022–2026.