Aaditya Ramdas

Source: https://stat.cmu.edu/~aramdas/ Parent: http://www.cs.cmu.edu/~scsfacts/perlis.html

Biography

Aaditya Ramdas is an Associate Professor (with tenure) at Carnegie Mellon University in the Department of Statistics and Data Science and the Machine Learning Department. He was a postdoc at UC Berkeley (2015–2018) mentored by Michael Jordan and Martin Wainwright, and obtained his PhD at CMU (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).

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 ASA's Pittsburgh Chapter, and is the program chair of AISTATS 2026.

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, including at Lunteren, AISTATS and VCMF, and invited tutorials at CUSO, KDD and ICML.

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). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, finance, fairness).

He co-organizes of the StatML Group at CMU. He loves to talk about backpacking adventures through over 70 countries, trash-free living, completing the Ironman triathlon, long-distance bicycle rides, books and parenthood. \

Curriculum Vitae

The E-book (publisher version)

Group

Courses, Workshops, Tutorials, Software, Talks, etc.

Advice for PhD students and assistant professors.These keywords quickly get my attention: - e-values and confidence sequences (supermartingales, testing by betting, sequential anytime-valid inference, optional stopping, peeking and p-hacking, change detection, game-theoretic statistics) - conformal prediction and calibration (distribution-free inference, uncertainty quantification for black-box machine learning, covariate/label shift, beyond exchangeability) - multiple testing and post-selection inference (false discovery rate, inference after model selection, online or interactive or bandit testing, post-hoc simultaneous inference) - high-dimensional, nonparametric statistics and machine learning (kernel methods, minimax rates, dimension-agnostic inference, universal inference, differential privacy, optimization) I work on “practical theory”, meaning that the vast majority of my papers are about designing theoretically principled algorithms that directly solve practical problems, and are usually based on simple, aesthetically elegant (in my opinion) ideas. A theoretician's goal is not to prove theorems, just as a writer's goal is not to write sentences. My goals are to improve my own (and eventually the field's) understanding of important problems, design creative algorithms for unsolved questions and figure out when and why they work (or don't), and often simply to ask an intriguing question that has not yet been asked.News I am the program chair (along with Arno Solin) of AISTATS 2026. I was invited to give a 2.5hr tutorial at ICML 2025 on game-theoretic statistics (e-values, confidence sequences, safe anytime-valid inference). I was elected to be an [IMS Fellow](https://imstat.org/2025/05/05/congratulations-to-the-202... [TRUNCATED: link-bomb detected]