School of Humanities and Sciences
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Associate Professor of Epidemiology and Population Health and, by courtesy, of Statistics and of Medicine (Stanford Prevention Research Center)
BioProfessor Baiocchi is a PhD statistician in Stanford University's Epidemiology and Population Health Department. He thinks a lot about behavioral interventions and how to rigorously evaluate if and how they work. Methodologically, his work focuses on creating statistically rigorous methods for causal inference that are transparent and easy to critique. He designed -- and was the principle investigator for -- two large randomized studies of interventions to prevent sexual assault in the settlements of Nairobi, Kenya.
Professor Baiocchi is an interventional statistician (i.e., grounded in both the creation and evaluation of interventions). The unifying idea in his research is that he brings rigorous, quantitative approaches to bear upon messy, real-world questions to better people's lives.
Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.
Mary V. Sunseri Professor in the School of Humanities and Sciences and Professor of MathematicsOn Leave from 09/01/2022 To 08/31/2023
Current Research and Scholarly InterestsResearch Interests:
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems.
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsResearch Interests:
Professor of Statistics and of Computer Science
BioEmily Fox is a Professor in the Department of Statistics and, by courtesy, Computer Science at Stanford University. Prior to Stanford, she was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer. Before joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.
Emily has been awarded a CZ Biohub Investigator Award, Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in large-scale Bayesian dynamic modeling, interpretability and computations, with applications in health and computational neuroscience.
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data SciencesOn Leave from 01/01/2023 To 03/31/2023
Current Research and Scholarly InterestsFlexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.
Professor of StatisticsOn Leave from 09/01/2022 To 08/31/2023
Current Research and Scholarly InterestsOur lab has been developing tools for the analyses of complex data structures, extending work on multivariate data to structured multitable table that include graphs, networks and trees as well as categorical and continuous measurements.
We created and support the Bioconductor package phyloseq for the analyses of microbial ecology data from the microbiome. We have specialized in developing interactive graphical visualization tools for doing reproducible research in biology.
John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research), of Epidemiology and Population Health and by courtesy, of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsMeta-research
Clinical and molecular epidemiology
Human genome epidemiology
Reporting of research
Empirical evaluation of bias in research
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science
Marjorie Mhoon Fair Professor of Quantitative Science and Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly InterestsEmpirical bias/shrinkage estimation; non-parametric, smoothing; statistical inverse problems.
Tze Leung Lai
Ray Lyman Wilbur Professor and Professor, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsResearch interests include clinical trial design, cancer biostatistics, survival analysis, adaptation and sequential experimentation, change-point detection and segmentation, stochastic optimization, time series and inference on stochastic processes, hidden Markov models and genomic applications.