Stanford University
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Aaditya Ramdas
Affiliate, Statistics
BioAaditya Ramdas is an Associate Professor (with tenure) in the Department of Statistics. He was a postdoc at UC Berkeley (2015–2018) mentored by Michael Jordan and Martin Wainwright, and obtained his PhD at Carnegie Mellon University (2010–2015) under Aarti Singh and Larry Wasserman, receiving the Umesh K. Gavaskar Memorial Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay (2005-09, All India Rank 47), from whom he recently received a Young Alumnus Achiever Award (2026).
His work has been recognized by the Presidential Early Career Award (PECASE), the highest distinction bestowed by the US government to young scientists. He has also received a Kavli fellowship from the National Academy of Sciences, a Sloan fellowship in Mathematics, the CAREER award from the National Science Foundation, the Emerging Leader Award from COPSS (Committee of Presidents of Statistical Societies), early career awards from the Bernoulli Society and the Institute of Mathematical Statistics, and faculty research awards from Adobe and Google. He was recently elected Fellow of the IMS, was awarded Statistician of the Year 2025 by the the American Statistical Associaton Pittsburgh Chapter. He was the program chair of AISTATS 2026, and the general chair of AISTATS 2027.
He has published over 150 peer-reviewed papers, about half at top journals like The Annals of Statistics, Biometrika, IEEE Transactions on Information Theory and PNAS, including prestigious discussion papers at the Journal of the Royal Statistical Society and Journal of the American Statistical Association, and about half at the top AI conferences like NeurIPS, ICML, ICLR, UAI and AISTATS, including over a dozen orals/spotlights. He has given several keynote talks invited tutorials.
Aaditya's research in mathematical statistics and learning has an eye towards designing algorithms that both have strong theoretical guarantees and also work well in practice. His main interests include post-selection inference (multiple testing, simultaneous inference), game-theoretic statistics (e-values, confidence sequences) and predictive uncertainty quantification (conformal prediction, calibration).
