School of Humanities and Sciences
Showing 1-8 of 8 Results
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Guenther Walther
Professor of Statistics
BioGuenther Walther studied mathematics, economics, and computer science at the University of Karlsruhe in Germany and received his Ph.D. in Statistics from UC Berkeley in 1994.
His research has focused on statistical methodology for detection problems, shape-restricted inference, and mixture analysis, and on statistical problems in astrophysics and in flow cytometry.
He received a Terman fellowship, a NSF CAREER award, and the Distinguished Teaching Award of the Dean of Humanities and Sciences at Stanford. He has served on the editorial boards of the Journal of Computational and Graphical Statistics, the Journal of the Royal Statistical Society, the Annals of Statistics, the Annals of Applied Statistics, and Statistical Science. He was program co-chair of the 2006 Annual Meeting of the Institute of Mathematical Statistics and served on the executive committee of IMS from 1998 to 2012. -
Wing Hung Wong
Stephen R. Pierce Family Goldman Sachs Professor of Science and Human Health and Professor of Biomedical Data Science
Current Research and Scholarly InterestsCurrent interest centers on the application of statistics to biology and medicine. We are particularly interested in questions concerning gene regulation, genome interpretation and their applications to precision medicine.
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Xiao Wu
Postdoctoral Scholar, Statistics
BioXiao Wu is a Data Science Fellow at Stanford Data Science, where he works with Professor Trevor Hastie in the Department of Statistics. His research interests lie in developing statistical and causal inference methods to address methodological needs in climate and health research. The key goal of his research is to provide scientific evidence on the health impacts of environmental factors in an age of rapidly changing climate.
Before coming to Stanford, he completed his Ph.D. training in the Department of Biostatistics at Harvard University, where he was advised by Dr. Francesca Dominici and Dr. Danielle Braun. His dissertation focuses on developing robust and interpretable causal inference methods to handle error-prone, continuous, and time-series exposures. He is also working on collaborative projects to design Bayesian clinical trials, meta-analyses, and real-world evidence studies.
