Robert Tibshirani, PhD, is Professor in the Departments of Statistics and Health Research and Policy. He was a Professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics. The recipient of numerous honors, Dr. Tibshirani was selected to receive the prestigious COPSS award in 1996. Given jointly by the worlds leading statistical societies, the award recognizes outstanding contributions to statistics by a statistician under the age of 40. He also co-authored three widely used books: Generalized Additive Models, An Introduction to the Bootstrap, and The Elements of Statistical Learning. He has a very strong training record, counting faculty at top-rated institutions among his students.
Richard Olshen, PhD, is Professor of Health Research and Policy and (by courtesy) of Electrical Engineering and of Statistics. He is chief of the Division of Biostatistics, one of three divisions of the Department of Health Research and Policy. Dr. Olshen is a co-inventor of CART technology and coauthor of the basic book, Classification and Regression Trees. His research has involved studies of longitudinal data, including those arising in gait analysis (regarding which he is coauthor of a basic text), cholesterol levels in human populations, renal physiology, and genetics. Recent efforts involve successive normalization of rectangular arrays of data, genome-wide association studies of mean arterial blood pressure, and strategies for treating certain patients with HIV/AIDS.
Chiara Sabatti, PhD, is an Associate Professor of Health Research and Policy and (by courtesy) of Statistics and is a member of Bio-X. Her research, awarded the NSF Career in 2003, is on statistical methods for the analysis of high-throughput genomic data, with special focus on understanding transcription regulation and the genetic basis of traits of interest. In the area of transcription regulation, she has proposed sparse Bayesian models to estimate signal from gene expression array experiments; she has developed genome-wide searching tools to identify binding sites of transcription factor; and she has used sparse factor models to describe regulatory networks. In the area of gene mapping, she has studied linkage disequilibrium, its measures, and variability across populations; she has proposed haplotype-based association tests; she has studied the properties of the raw signals of genotyping arrays, designing calling algorithms as well as using them to identify copy number variants; she has been involved in research that attempts to correct for unknown population stratification and relatedness in genome-wide association studies. She was at UCLA from 2000 to 2009, rising in rank from assistant to full professor. While at UCLA, she was a mentor in four Training Grants, all dealing with quantitative methods for biology and genomics. She contributed to designing the interdepartmental PhD program in Bioinformatics, and she was a member of its executive committee during its first year.
Marc Coram, PhD, is Assistant Professor of Health Research and Policy. His research develops theory and methodology to perform statistical inference about the latent structure of complex systems. He is collaborating with individuals in the Cancer Center (such as Dr. Garry Nolan and his students) working with data from phospho-flow cytometry, protein arrays, and multiplex RT-PCR.
Bradley Efron, PhD, is Professor of Statistics and Health Research and Policy. Over the last decade, Efron has published a large body of work concerning the analysis of very large biomedical data sets. These consider the role of empirical Bayes methodology, particularly as it relates to false discovery rates. Combining theoretical development with the analysis of data sets from the Stanford Medical School, both the virtues and drawbacks of commonly used methods such as the Benjamini--Hochberg algorithm are investigated.
Trevor Hastie, PhD, is Professor of Statistics and Health Research and Policy. He specializes in applied regression and classification methodology, and has written two books in this area: Generalized Additive Models (with R. Tibshirani, Chapman and Hall 1991), and Elements of Statistical Learning (second edition) (with R. Tibshirani and J. Friedman, Springer 2009). His current research focuses on applied problems in biology and genomics, medicine, and industry, in particular data mining, prediction, and classification problems. He collaborates with Professors Howard Chang, Matt van de Rijn, and Robert West, of the Department of Pathology.
Susan Holmes, PhD, is Professor of Statistics and specializes in the use and development of multivariate statistical tools and methods for biological data. She collaborates with PIs in the School of Medicine to analyze multivariate data such as gene expression patterns and bacterial abundance in conjunction with network or tree information. She has developed image and video segmentation tools for tracking cell populations in lymph nodes and provides spatial analysis of the data collected that enable a better understanding of the cancer immune interactions. Collaborators include R. Shafer, P. Lee, and J. Frydman.
Iain Johnstone, PhD, is Professor of Statistics and Health Research and Policy (Biostatistics). His research is in the area of statistical signal processing and high-dimensional data analysis. In statistical signal processing, he has been interested in applying multiresolution notions such as wavelet bases to the nonparametric estimation of functions observed directly or indirectly in noise. In high-dimensional data analysis, he has been interested in applying ideas from random matrix theory to study properties of classical multivariate statistical procedure. He has been Principal Investigator for an NIH grant, with Trevor Hastie and Rob Tibshirani, "Statistical Methods for Biomedical Signals and Images," now in its 14th year.
Tze Leung Lai, PhD, is Professor of Statistics and (by courtesy) of Health Research and Policy and of the Institute of Computational and Mathematical Engineering. He is the co-Director of the Biostatistics Core of the Cancer Center and of the Center for Innovative Study Design at the Stanford School of Medicine, and is also the Director of the Financial Mathematics Program in the Department of Statistics. He has published over 240 papers and has supervised over 50 PhD theses and has written eight books.
Philip W. Lavori, PhD, is Professor and Chair of the Department of Health Research and Policy and (by courtesy) Professor of Statistics. His research is on statistical designs for randomized clinical trials in which patient preferences and changes in patient state in response to treatments over time play a role in the evolving choice of treatments. He is co-Director of Spectrum, the Stanford Clinical and Translational Sciences Award, and Director of the Spectrum Biostatistics and Study Design Program, Director of the Biostatistics Shared Resource for the Stanford Comprehensive Cancer Center, and Biostatistics Core Leader for two Program Projects.
Ying Lu, PhD, is Professor of Health Research and Policy and Director of the Palo Alto Cooperative Studies Program Coordinating Center, Department of Veterans Affairs. Dr. Lus research areas are statistical methods for classifications and outcomes research, medical diagnosis, design and implementing of clinical trials, quality control and quality assurance of imaging studies, and statistical applications in radiology, oncology, bone, and neurological diseases. Dr. Lus research has been supported by NIH and foundation grants. Dr. Lu is a member of NIH study section and FDA PCND advisory panel, AJCC Cancer Staging Statistical Task Forces.
Lorene Nelson, PhD, is Associate Professor of Health Research and Policy and the co-founder and former Director of the Graduate Program in Epidemiology at the Stanford School of Medicine. Since 1994, she has taught an advanced course in epidemiologic and clinical research methods to masters and doctoral students in the training program. She has mentored many students at the doctoral, pre-doctoral, and clinical fellow levels. Her research focuses on the epidemiology of neurodegenerative disorders, including Parkinsons disease, amyotrophic lateral sclerosis and multiple sclerosis, and genetic epidemiology and epidemiologic methods including techniques quantifying gene-environment interaction.
Art Owen, PhD, is Professor of Statistics. His research interests include nonparametric inference (empirical likelihood, bootstrap, cross-validation), quasi-Monte Carlo sampling, and analysis of high-dimensional transposable data sets. For the past ten years he was worked on statistical problems motivated by developments in biology, resulting in publications in PLoS and other outlets. Some of the underlying statistical ideas have been distilled into papers for the Annals of Statistics and the Annals of Applied Statistics.
Bala Rajaratnam, PhD, is Assistant Professor of Statistics. His research focuses on the development of statistical methods related to high-dimensional covariance estimation with applications to gene network interaction analysis. On the theoretical side the emphasis has been on developing a statistically rigorous framework for detecting gene network interactions in high-throughput data, even in the high-dimensional, low sample-size setting. A strong focus is on developing tractable Bayesian approaches to high-dimensional statistical inference in such settings. He is a member of the Cardiovascular Institute and Bio-X program at the Stanford School of Medicine.
David Siegmund, PhD, is Professor of Statistics. His research until the middle 1980s focused on sequential analysis, with motivation coming primarily from the design and analysis of sequential clinical trials. Since then he has worked on change-point and other nonlinear regression problems where one seeks to detect and identify a structured sparse signal in noisy data. Since the early 1990s his primary focus has been on problems of statistical genetics, especially gene mapping, and related problems of molecular biology, e.g., using change-point methods to detect regions of copy number variation.
Mei-Chiung Shih, PhD, is Assistant Professor of Health Research and Policy. Her research interests include clinical trial design and analysis, sequential and adaptive experimentation and analysis, longitudinal data analysis, biomarker-based personalized medicine, vaccine safety monitoring, and comparative effectiveness research. Dr. Shih is also Acting Associate Director for Science and Senior Biostatistician at the Department of Veterans Affairs (VA) Palo Alto Cooperative Studies Program Coordinating Center, which is one of the five national statistical coordinating centers at VA that design, implement, coordinate, and analyze large multi-center randomized clinical trials in the veteran population.
Hua Tang, PhD, is Associate Professor in the Department of Genetics and (by courtesy) of Statistics. Research in her laboratory develops and applies statistical methods for analyzing patterns of human genetic variation, which underlie the phenotypic diversity of our species. Dr. Tang is collaborating on various genome-wide studies focusing on stratified or recently admixed populations. These studies offer unique opportunities to elucidate the evolutionary forces that have shaped the patterns of genetic variation in humans, to uncover the genetic basis of complex traits, and to shed light on the mechanisms that lead to diverse phenotypes and disparate disease risks among populations.
Jonathan Taylor, PhD, is Associate Professor of Statistics. His research focuses on the development of statistical methods related to smooth random fields and their applications in neuroimaging. He has developed multiple comparison tools based on Random Field Theory (RFT) and False Discovery Rate (FDR) for non-stationary models, both on the theoretical side, yielding approximate formulae for tail probabilities; as well as the practical side, developing software and algorithms to estimate quantities in these formulae as well as to fit linear models to fMRI data. He is a member of the core Mathematics and Computational Sciences Program.
Lu Tian, ScD, is Assistant Professor of Health Research and Policy. He received ScD in Biostatistics from Harvard University. He has been working with Dr. McDermott on several large NHLBI-funded longitudinal studies of PAD for nearly five years at Northwestern University. He has rich experience in statistical methodological research as well as planning epidemiological study and applied data analysis. His current research interest is in developing statistical methods for survival analysis, causal inference and analyzing high-dimensional data.
Guenther Walther, PhD, is Professor of Statistics. He has been working on various problems in nonparametric and multivariate statistics such as mixture models and related problems in unsupervised learning, shape-restricted inference, the estimation of multivariate sets, and multivariate detection problems. Particular focuses of his research have been statistical optimality properties and computational issues. Concurrently he has been working on statistical problems in astrophysics, and more recently on the statistical analysis of flow cytometry data, with an emphasis on identifying subpopulations of cells and on developing quantitative methods for comparing subsets of cells.
Alice S. Whittemore, PhD, is Professor of Health Research and Policy. Her research focuses on the development of improved statistical methods for the design and conduct of studies related to the molecular and genetic epidemiology of site-specific cancers. This work has earned her the Eighth Annual American Association for Cancer Research-American Cancer Society Award for Research Excellence in Cancer Epidemiology and Prevention, as well as the Janet L. Norwood Award and the Florence Nightingale Award for outstanding achievement by a woman in the statistical sciences.
Wing Wong, PhD, is Professor of Statistics and Health Research and Policy. The current emphasis of his lab includes gene regulatory analysis and RNA-seq analysis, and they are initiating a new research program in personalized medicine. All these areas require new methods in statistical analysis and computation. He has a track record of contributing novel concepts and useful methods to mainstream statistics and to bioinformatics. Examples include the use of Monte Carlo algorithms in Bayesian computation, asymptotic inference in high- or infinite-dimensional problems, and bioinformatics tools such as dChIP (Li and Wong 2001) for the analysis of gene expression microarray data and CisModule (Zhou and Wong 2004) for the computational prediction of cis-regulatory modules. Finally they have recently developed new methods for detecting epistasis in disease association studies (Li et al 2010). Wongs lab has active ongoing collaborations with biomedical sciences within Stanford including the labs of Matt Scott, Julie Baker, Mylene Yao, and Doug Levinson.