School of Humanities and Sciences
Showing 51-100 of 189 Results
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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. -
Andrew Mosser Hong
Masters Student in Management Science and Engineering, admitted Autumn 2022
Peer Advisor, StatisticsCurrent Role at StanfordMasters student in Management Science & Engineering. Undergraduate student in Data Science. Peer Advisor in Data Science. Resident Assistant.
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Aditi Jha
Postdoctoral Scholar, Statistics
BioI am a computational neuroscientist, working at the intersection of machine learning and systems neuroscience.
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Haoran Jia
Masters Student in Statistics, admitted Autumn 2024
BioHello! I'm Haoran Jia, a M.S. Statistics Data Science candidate. With proficiency in Python, R, and SQL, I have prior experience in providing data science insights for startups, developing ML models for data science software, and researching topics related to deep learning and statistical inference. My passion for data science drives me to continuously engage in projects in the fields of DS, ML, and LLM.
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Iain Johnstone
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.
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Cindy Kirby
Administrative Associate, Statistics
Current Role at StanfordFaculty Support: Efron, Diaconis, Lai
Department Webmaster
Building Manager
Space Coordinator
Property Administrator
Sustainability Partner
LaTeX Local Expert -
Percy Liang
Associate Professor of Computer Science, Senior Fellow at the Stanford Institute for HAI, and Associate Professor, 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) and the director of the Center for Research on Foundation Models (CRFM). He is currently focused on making foundation models (in particular, language models) more accessible through open-source and understandable through rigorous benchmarking. In the past, he has worked on many topics centered on machine learning and natural language processing, including robustness, interpretability, human interaction, learning theory, grounding, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. 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), a Microsoft Research Faculty Fellowship (2014), and paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS.
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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.
