Stanford University
Showing 101-120 of 189 Results
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Qiao Liu
Postdoctoral Scholar, Statistics
BioI am currently a postdoctoral scholar at the Department of Statistics, Stanford University, advised by Prof. Wing Hung Wong. I will be joining the Department of Biostatistics, Yale University as an tenure-track assistant professor at 2025 Fall. My general research interest lies in the multi-disciplinary area where I have been committed to developing practical statistical and machine learning tools with significance in both statistical theory and applications. In particular, I have been pursuing this research agenda by exploiting the advances in generative artificial intelligence (AI) to tackle several fundamental statistical problems, such as density estimation, causal inference, and unsupervised learning with also broad applications in computational biology.
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Andrea Montanari
John D. and Sigrid Banks Professor and Professor of Mathematics
BioI am interested in developing efficient algorithms to make sense of large amounts of noisy data, extract information from observations, estimate signals from measurements. This effort spans several disciplines including statistics, computer science, information theory, machine learning.
I am also working on applications of these techniques to healthcare data analytics. -
Tim Morrison
Ph.D. Student in Statistics, admitted Autumn 2020
BioI am a fourth-year PhD student in Statistics. I am fortunate to be advised by Professor Art Owen and also to work with Professor Mike Baiocchi. I am also grateful to have received the B. C. and E. J. Eaves Stanford Graduate Fellowship. My research interests include constrained experimental design and causal inference.
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Art Owen
Max H. Stein Professor
On Partial Leave from 10/01/2024 To 12/31/2024Current Research and Scholarly InterestsStatistical methods to analyze large data matrices in bioinformatics
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Julia Palacios
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics and public health . My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory for big data.
