Stanford University
Showing 101-145 of 145 Results
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Julia Salzman
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics and of Biology
Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
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Siamak Sorooshyari
Postdoctoral Scholar, Statistics
BioMy research lies at the intersection of AI/ML, statistics, biology, and engineering. I was initially trained as an electrical engineer, with a focus on signal processing and statistical algorithms. I then pursued my PhD in a neuroscience laboratory studying stress and the blood-brain barrier, where I gained experience with biological systems, experimental design, instrumentation, and data collection. My current work brings these perspectives together as I develop computational and statistical methods to better understand and predict biological processes.
A major focus of my research is understanding how aging affects the brain and how these changes are reflected across biological scales and measurement modalities. I have studied signals recorded from individual brain regions, communication between brain networks, and changes in functional connectivity across the lifespan. An important question in this work is whether quantitative properties of biological signals, such as monotonicity, exhibit consistent relationships with age. I am particularly interested in determining how different modalities capture changes associated with aging in both healthy and diseased systems, and what these measurements reveal about the underlying biological processes. This perspective can also provide insight into the reliability and interpretability of different recording modalities as measures of biological aging. I have recently begun extending these questions beyond the brain to the brain-gut-heart axis in healthy humans. By examining relationships and coordinated changes among measurements from multiple organs, I aim to develop a more integrated understanding of healthy aging and, ultimately, of how these relationships are altered in disease, specifically neurodegeneration. This work represents a broader effort to study aging as a multidimensional biological process rather than as a phenomenon confined to a single organ or measurement modality.
In parallel, I develop statistical methods for assessing the reliability and reproducibility of unsupervised learning results. In particular, I am interested in understanding how methodological choices - including the clustering algorithm, model parameters, and the number of clusters - can affect the conclusions drawn from noisy, high-dimensional datasets. This work has led to ERICA (evaluating replicability via iterative clustering assignments), a framework for evaluating whether clustering structure can be reproduced under repeated analyses without requiring a predefined ground truth. I am applying this framework to biological datasets, including cancer and neurodegenerative diseases, where clustering is frequently used to identify molecular or phenotypic subgroups. More broadly, this work seeks to develop rigorous statistical tools that can help distinguish reproducible structure from patterns that may depend strongly on methodological choices. -
Paul Switzer
Professor of Statistics and of Environmental Earth System Science, Emeritus
BioDr. Switzer's research interests are in the development of statistical tools for the environmental sciences. Recent research has focused on the interpretation of environmental monitoring data, design of monitoring networks, detection of time trends in environmental and climatic paramenters, modeling of human exposure to pollutants, statistical evaluation of numerical climate models and error estimation for spatial mapping.
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Hua Tang
Professor of Genetics and, by courtesy, of Statistics
Current Research and Scholarly InterestsDevelop statistical and computational methods for population genomics analyses; modeling human evolutionary history; genetic association studies in admixed populations.
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Lu Tian
Professor of Biomedical Data Science and, by courtesy, of Statistics
Current Research and Scholarly InterestsMy research interest includes
(1) Survival Analysis and Semiparametric Modeling;
(2) Resampling Method ;
(3) Meta Analysis ;
(4) High Dimensional Data Analysis;
(5) Precision Medicine for Disease Diagnosis, Prognosis and Treatment. -
Robert Tibshirani
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsMy research is in applied statistics and biostatistics. I specialize in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
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Brian Trippe
Assistant Professor of Statistics and, by courtesy, of Computer Science
BioDr. Brian Trippe is an assistant professor at Stanford in the Department of Statistics, with an affiliation in Stanford Data Science.
In his research, Dr. Trippe develops probabilistic machine learning methods to address challenges in biotechnology and medicine. Recently, his focus has been on generative modeling and inference algorithms for protein engineering.
Before joining Stanford, Dr. Trippe was a postdoctoral fellow at Columbia University in the Department of Statistics, and a visiting researcher at the Institute for Protein Design at the University of Washington. -
Guenther Walther
John A. Overdeck Professor
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. -
Mohan Wang
Masters Student in Statistics, admitted Autumn 2026
BioI obtained my Bachelor of Science degree from Emory University, double-majoring in Quantitative Sciences and Sociology. In Statistics, my interests lie broadly in causal inference.
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Tijana Zrnic
Assistant Professor of Statistics, of Management Science and Engineering and, by courtesy, of Computer Science
BioTijana Zrnic is an Assistant Professor at Stanford University, jointly appointed between Statistics, Management Science & Engineering, and, by courtesy, Computer Science. She works on foundational questions in machine learning, statistics, and data-driven decision-making. Example topics of interest include AI-assisted statistical inference and data collection, performative prediction, and studying selection bias.
