School of Humanities and Sciences
Showing 1-50 of 131 Results
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Jose Bolorinos
Ph.D. Student in Civil and Environmental Engineering, admitted Autumn 2017
Masters Student in Statistics, admitted Winter 2020BioJose Bolorinos is a Ph.D. candidate in Civil and Environmental Engineering (Atmosphere and Energy) and an M.S. student in Statistics. Jose's research focuses on data-driven, systems-level strategies for coordinating urban water and energy supply infrastructure. As part of this work, Jose has investigated policy approaches that better understand and manage the lifecycle impacts of the energy sector on watersheds, air quality, and carbon emissions. Jose has also developed closed-loop customer monitoring and segmentation tools that allow water and electricity utilities to quickly track the responses of their customers to demand shocks inside and outside of their service areas. Currently, Jose is developing data-driven methods to support optimal design and deployment of the next generation of anaerobic, membrane-based wastewater treatment systems.
Broadly, Jose's research aims to develop open-source, cost-effective data and computation tools for infrastructure planning and management. His work has been featured at the California Data Collaborative, Stanford's Big Earth Water Hackathon, and AI for Climate Change Initiative. In 2018, a visualization tool Jose developed for wastewater treatment plant operators was awarded the Best Software Prize at Stanford's Big Earth Water Hackathon.
Prior to coming to Stanford, Jose worked as a data scientist for a healthcare consultancy subcontracted by the federal government to manage its Medicare and Medicaid claims databases. Jose received a B.A. in Economics from UC Berkeley and an M.S. in Environmental Engineering and Science from Stanford University. He was part of the start up operations team at the Bill & Cloy Resource Recovery Center, an experimental, pilot-scale wastewater treatment facility launched recently on the Stanford campus to accelerate innovative approaches to wastewater treatment. -
Emmanuel Candes
Barnum-Simons Chair in 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. -
Nicole Cobb
Grants Assistant & Administration Associate, Statistics
BioNicole Cobb is the Grants Assistant & Administration Associate with the Statistics Department in the School of Humanities & Sciences.
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Persi Diaconis
Mary V. Sunseri Professor and Professor of Mathematics
Current Research and Scholarly InterestsResearch Interests:
PROBABILITY THEORY
BAYESIAN STATISTICS
STATISTICAL COMPUTING
COMBINATORICS -
David Donoho
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.
Research Statement:
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. -
John Duchi
Assistant Professor of Statistics and of Electrical Engineering
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.
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Bradley Efron
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsResearch Interests:
BOOTSTRAP
BIOSTATISTICS
BAYESIAN STATISTICS -
Trevor Hastie
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.
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Susan Holmes
Professor of Statistics
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
C. F. Rehnborg Professor in Disease Prevention in the School of Medicine, Professor of Medicine, of Health Research and Policy (Epidemiology) and by courtesy, of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsMeta-research
Evidence-based medicine
Clinical and molecular epidemiology
Human genome epidemiology
Research design
Reporting of research
Empirical evaluation of bias in research
Randomized trials
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science -
Pratheepa Jeganathan
Postdoctoral Research Fellow, Statistics
Current Research and Scholarly InterestsStatistical methods for longitudinal human microbiome data
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Iain Johnstone
Marjorie Mhoon Fair Professor in 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.
