Stanford University
Showing 51-100 of 165 Results
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Brice Huang
Postdoctoral Scholar, Statistics
BioBrice Huang is a Stanford Science Fellow and NSF postdoctoral fellow in the Department of Statistics, hosted by Andrea Montanari. He received his PhD in Electrical Engineering and Computer Science at MIT advised by Guy Bresler and Nike Sun.
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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 Human-Centered AI, 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
BioScott Linderman, PhD, is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute, as well as the Co-Director of the Stanford Center for Neural Data Science. His research focuses on machine learning, computational neuroscience, and the general question of how computational and statistical methods can help to decipher neural computation. His work combines novel methodological development in the areas of state space models, deep generative models, point processes, and approximate Bayesian inference with applied statistical analyses of large-scale neural and behavioral data. Previously, he was a postdoctoral fellow at Columbia University and a graduate student at Harvard University. His work has been recognized with a Savage Award from the International Society for Bayesian Analysis, an AISTATS Best Paper Award, an NSF CAREER Award, and fellowships from the McKnight, Sloan, and Simons Foundations.
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Andrea Montanari
John D. and Sigrid Banks Professor and Professor 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. -
Art Owen
Max H. Stein Professor
Current Research and Scholarly InterestsStatistical methods to analyze large data matrices in bioinformatics
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Julia Palacios
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios’s research spans Bayesian nonparametrics, probabilistic AI, stochastic processes, and computational statistics. Her group develops stochastic models and efficient inference algorithms for understanding evolutionary dynamics in population genetics, infectious diseases and cancer.
