PeopleofSuperintelligence

Researcher

Aaditya Ramdas

Aaditya Ramdas is a statistician at Stanford University who develops methods for machine learning and statistical inference. He studied computer science at IIT Bombay and completed a doctorate in statistics and machine learning at Carnegie Mellon in 2015. After postdoctoral work with Michael Jordan and Martin Wainwright at UC Berkeley, he joined Carnegie Mellon’s faculty in 2018. His coauthored work extended conformal prediction to changing data distributions in 2019 and developed confidence bounds for contextual bandit evaluation in 2021; a 2022 paper explained methods that remain valid as researchers monitor data and decide when to stop. He received a Sloan Research Fellowship in Mathematics in 2024 and the Presidential Early Career Award for Scientists and Engineers in 2025, and joined Stanford’s Department of Statistics as an associate professor in September 2026.

Works
312
Citations
4,529
h-index
29

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2005–2015

    IIT Bombay and Carnegie Mellon

    He earns a computer science degree at IIT Bombay in 2009. In 2015, he completes a PhD in statistics and machine learning at Carnegie Mellon University, advised by Aarti Singh and Larry Wasserman.

  2. 2015–2018

    Postdoctoral research at Berkeley

    From 2015 to 2018, he works as a postdoctoral researcher in electrical engineering and computer sciences and statistics at UC Berkeley. Michael Jordan and Martin Wainwright mentor him.

  3. 2018

    Faculty at Carnegie Mellon

    In 2018, he joins Carnegie Mellon’s faculty, with appointments in the Machine Learning and Statistics and Data Science departments.

  4. 2019

    Conformal prediction under covariate shift

    In 2019, he coauthors “Conformal Prediction Under Covariate Shift” with Ryan J. Tibshirani, Rina Foygel Barber and Emmanuel Candes. The paper develops weighted prediction intervals for settings in which training and test covariate distributions differ, using a known or accurately estimated likelihood ratio between those distributions.

  5. 2021

    Off-policy confidence sequences

    With Nikos Karampatziakis and Paul Mineiro, he coauthors “Off-Policy Confidence Sequences” at ICML in 2021. The paper develops confidence bounds for contextual bandit evaluation that remain valid over time, including when an analyst chooses when to stop collecting data.

  6. October 2022

    Safe anytime-valid inference

    In October 2022, he coauthors “Game-theoretic statistics and safe anytime-valid inference” with Peter Grünwald, Vladimir Vovk and Glenn Shafer. The paper explains e-processes for testing hypotheses and confidence sequences for estimation that remain valid during continuous data monitoring and optional stopping.

  7. 2024

    A Sloan research fellowship

    He receives an Alfred P. Sloan Research Fellowship in Mathematics in 2024. His research includes methods for quantifying uncertainty in AI predictions and measuring statistical evidence through game-theoretic principles.

  8. October 2024

    A book on e-values

    In October 2024, he and Ruodu Wang release a preprint of the book “Hypothesis testing with e-values”. It covers universal inference, e-processes and the use of e-values for multiple testing.

  9. 2025

    A Presidential Early Career Award

    In 2025, he receives the Presidential Early Career Award for Scientists and Engineers (PECASE), with funding from the National Science Foundation.

  10. September 2026

    Associate professor at Stanford

    In 2026, he serves as an AISTATS program chair with Arno Solin. In September 2026, he joins Stanford University’s Department of Statistics as an associate professor. His research spans statistical inference and machine learning, with a focus on reproducibility, sequential decision-making and uncertainty quantification.

Career

  1. Since 2026Stanford UniversityAssociate Professor of Statistics
  2. 2018–2026Carnegie Mellon UniversityFaculty
  3. 2015–2018University of California, BerkeleyPostdoctoral Researcher

Education

  1. 2015Carnegie Mellon UniversityPhD in statistics and machine learning
  2. 2009IIT BombayDegree in computer science

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Aaditya Ramdas - College of Engineering at Carnegie Mellon University engineering.cmu.edu. Checked 27 October 2026.
  2. Ramdas Receives Presidential Early Career Award - Statistics & Data Science - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University cmu.edu. Checked 27 October 2026.
  3. Aaditya Ramdas' Webpage web.stanford.edu. Checked 27 October 2026.
  4. Aaditya Ramdas appointed Associate Professor | Department of Statistics statistics.stanford.edu. Checked 27 October 2026.
  5. Ramdas Honored With Bernoulli Society's New Researcher Award cs.cmu.edu. Checked 27 October 2026.
  6. Conformal Prediction Under Covariate Shift proceedings.neurips.cc. Checked 27 October 2026.
  7. Off-Policy Confidence Sequences proceedings.mlr.press. Checked 27 October 2026.
  8. [2210.01948] Game-theoretic statistics and safe anytime-valid inference arxiv.org. Checked 27 October 2026.
  9. Ramdas Named Alfred P. Sloan Research Fellow in Mathematics | Dietrich College of Humanities and Social Sciences cmu.edu. Checked 27 October 2026.
  10. [2410.23614] Hypothesis testing with e-values arxiv.org. Checked 27 October 2026.
  11. Aaditya Ramdas | Department of Statistics statistics.stanford.edu. Checked 27 October 2026.