Wentao Zhan

University of Wisconsin-Madison. Madison, Wisconsin, 53706, U.S.

prof_pic.jpg

wentao.zhan at wisc dot edu

I’m Wentao Zhan, curently a postdoctoral fellow in the Department of Statistics at University of Wisconsin-Madison working with Dr. Matthias Katzfuss. I obtained my Ph.D. degree from the Department of Biostatistics at Johns Hopkins University, advised by Dr. Hongkai Ji and Dr. Abhirup Datta. Before beginning my Ph.D. in 2020, I earned a Bachelor of Science in Mathematics and Applied Mathematics from Fudan University.

My research addresses key statistical challenges at the frontiers of science, with a particular focus on spatial data across diverse contexts. Methodologically, I aim to blend the flexibility of modern machine learning with the parsimony of traditional geospatial modeling to develop scalable methods that retain clear statistical interpretability. For instance, my recent work, “Neural Networks for Geospatial Data”, presents an innovative and efficient adaptation of neural networks for spatial data, underpinned by rigorous theoretical analysis.

In terms of application, my primary focus is on spatial transcriptomics—a cutting-edge technology that captures spatially-resolved gene expression data for thousands of cells and genes simultaneously. Through spatial modeling, my work aims to uncover the interactions between cells and their microenvironments, offering insights that could inform immunotherapy and pathology research. I also apply my methods to environmental data. I developed the Python package GeospaNN, which implements my previous work and performs well on PM 2.5 data from the U.S. Environmental Protection Agency (EPA).

For more details on my research, please visit my Research page.

Awards

  • UW Distinguished Research Fellowship, University of Wisconsin-Madison, 2025
  • Margaret Merrell Award for Excellence in Research, Johns Hopkins Department of Biostatistics, 2025
  • JASA Reproducibility Award, JASA Editorial board, 2025
  • Travel Award, ASA Section on Statistics and the Environment (ENVR), 2024
  • Travel Grants Award, Johns Hopkins School of Public Health, 2023
  • Outstanding Graduates, Fudan University, 2020

Key words:

Methodology: Spatial Statistics, Gaussian Process, Machine Learning, High-dimensional Data.
Application: Spatial Transcriptomics, Air Pollution, Genomics.