School of Humanities and Sciences
Showing 1-38 of 38 Results
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 Mathematics
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, by courtesy, 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 Sciences
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 04/01/2022 To 06/30/2022
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.
Associate Professor of Computer Science and, by courtesy, of Statistics
BioPercy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Scott W Linderman
Assistant Professor of Statistics and, by courtesy, of Computer Science and of Electrical Engineering
BioScott is an Assistant Professor of Statistics and, by courtesy, Electrical Engineering and Computer Science at Stanford University. He is also an Institute Scholar in the Wu Tsai Neurosciences Institute and a member of Stanford Bio-X and the Stanford AI Lab. His lab works at the intersection of machine learning and computational neuroscience, developing statistical methods to analyze large scale neural data. Previously, Scott was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University, and he completed his PhD in Computer Science at Harvard University with Ryan Adams and Leslie Valiant. He obtained his undergraduate degree in Electrical and Computer Engineering from Cornell University and spent three years as a software engineer at Microsoft before graduate school.
Robert and Barbara Kleist Professor in the School of Engineering and Professor of Statistics and, by courtesy, 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.
Max H. Stein ProfessorOn Leave from 01/01/2022 To 06/30/2022
Current Research and Scholarly InterestsStatistical methods to analyze large data matrices in bioinformatics
Assistant Professor of Statistics, of Biomedical Data Science and, by courtesy, of Biology
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.
Professor of Statistics and of Economics
Current Research and Scholarly InterestsWork in progress is described under "Projects"
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsStatistical models and reasoning are key to our understanding of the genetic basis of human traits. Modern high-throughput technology presents us with new opportunities and challenges. We develop statistical approaches for high dimensional data in the attempt of improving our understanding of the molecular basis of health related traits.
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics
Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
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.
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.
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.
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.
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.