David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
Statistics
Bio
David 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.
2024-25 Courses
- Multivariate Analysis and Random Matrices in Statistics
STATS 325 (Spr) - Resampling Methods: Bootstrap, Cross Validation and Beyond
STATS 208 (Spr) - The Data Science of Disinformation
DATASCI 194D, DATASCI 294D (Aut) -
Independent Studies (2)
- Industrial Research for Statisticians
STATS 398 (Aut, Win, Spr, Sum) - Research
STATS 399 (Aut, Win, Spr, Sum)
- Industrial Research for Statisticians
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Prior Year Courses
2023-24 Courses
- Bootstrap, Cross-Validation, and Sample Re-use
STATS 208 (Win) - Massive Computational Experiments, Painlessly
STATS 285 (Aut) - The Challenge Problems Paradigm in Empirical Machine Learning and Beyond
STATS 335 (Aut)
2022-23 Courses
- Function Estimation in White Noise
STATS 322 (Spr) - Introduction to Statistical Learning
STATS 216 (Win)
2021-22 Courses
- Introduction to Nonparametric Statistics
STATS 205 (Spr) - Multivariate Analysis and Random Matrices in Statistics
STATS 325 (Win)
- Bootstrap, Cross-Validation, and Sample Re-use
Stanford Advisees
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Postdoctoral Faculty Sponsor
Milad Bakhshizadeh, Elad Romanov, Siamak Sorooshyari -
Doctoral Dissertation Advisor (AC)
Apratim Dey -
Doctoral Dissertation Co-Advisor (AC)
Josh Kazdan