School of Humanities and Sciences
Showing 1-50 of 184 Results
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Ethan Allavarpu
Masters Student in Statistics, admitted Autumn 2022
Project Assistant, Healthy Planet Healthy PeopleBioHello! My name is Ethan, and I am pursuing a Master of Science (M.S.) in Data Science at Stanford University (with coursework in Statistics, Computer Science, and Computational and Mathematical Engineering). Before Stanford, I graduated summa cum laude with a Bachelor of Science (B.S.) in Statistics from the University of California, Los Angeles (UCLA). I am always eager to contribute to research and gain more experience through data science internships. My technical prowess, determined work ethic (I completed my four-year undergraduate degree at UCLA in three years), and effective communication skills make me a valuable addition to any team.
I am currently a Data Science Intern at Apple, working on data processing, visualization, and modeling (ML). I am also a research assistant within the Luby Lab at Stanford, working on processing, standardizing, and visualizing data regarding brick kiln production in South Asia. Last year, I interned with Bridg as a Data Science Intern, working on data querying, data transformations, natural language processing (NLP), and machine learning with Python and SQL--with integrations in Snowflake (and Snowpark, Snowflake's Python API)--on terabytes of data (over 100 billion observations). My projects improved insights from product descriptions and standardized features across multiple sources.
My experiences have prepared me to work in virtually any domain. I am always willing to discuss potential work opportunities or my path with prospective undergraduate or graduate students or data science enthusiasts via LinkedIn or email. -
Michael Baiocchi
Associate Professor of Epidemiology and Population Health and, by courtesy, of Statistics and of Medicine (Stanford Prevention Research Center)
BioProfessor Baiocchi is a PhD statistician in Stanford University's Epidemiology and Population Health Department. He thinks a lot about behavioral interventions and how to rigorously evaluate if and how they work. Methodologically, his work focuses on creating statistically rigorous methods for causal inference that are transparent and easy to critique. He designed -- and was the principle investigator for -- two large randomized studies of interventions to prevent sexual assault in the settlements of Nairobi, Kenya.
Professor Baiocchi is an interventional statistician (i.e., grounded in both the creation and evaluation of interventions). The unifying idea in his research is that he brings rigorous, quantitative approaches to bear upon messy, real-world questions to better people's lives. -
Milad Bakhshizadeh
Postdoctoral Scholar, Statistics
Current Research and Scholarly InterestsHigh dimensional Statistics, Concentration inequalities, Random Matrix Theory, Structured signal processing, Inverse Problems, Phase Retrieval.
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Emmanuel Candes
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. -
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 in the School of Humanities and Sciences 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
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.
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Bradley Efron
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsResearch Interests:
BOOTSTRAP
BIOSTATISTICS
BAYESIAN STATISTICS -
Emily Fox
Professor of Statistics and 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. -
Daniel Frees
Masters Student in Statistics, admitted Autumn 2023
BioDaniel Frees is an M.S. Data Science student in the Stanford Statistics department. He is also a Data Scientist at IBM. He is passionate about using data to drive advances in personalized medicine and fitness.
