Aaditya Ramdas


Aaditya Ramdas (PhD, 2015)
Assistant Professor
Department of Statistics and Data Science (75%)
Machine Learning Department (25%)
Carnegie Mellon University

132H Baker Hall
aramdas AT {empty or stat or cs} DOT cmu FULLSTOP edu

These keywords quickly get my attention

  • game-theoretic probability and statistics (confidence sequences, e-processes, supermartingales, testing by betting, sequential inference, optional stopping, peeking and p-hacking, changepoint detection)

  • conformal prediction and calibration (distribution-free inference, uncertainty quantification for black-box machine learning, exchangeability and beyond, structured problems)

  • multiple hypothesis testing and post-selection inference (false discovery or coverage rate, online or interactive or active or bandit or structured settings, post-hoc analysis)

  • 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.

Selected recent papers (for all papers, see this page (by topic) or my CV (by year))

  • A composite generalization of Ville's martingale theorem
    J. Ruf, M. Larsson, W. Koolen, A. Ramdas       arxiv

  • E-detectors: a nonparametric framework for online changepoint detection
    J. Shin, A. Ramdas, A. Rinaldo       arxiv  

  • Conformal prediction beyond exchangeability
    R. Barber, E. Candes, A. Ramdas, R. Tibshirani       arxiv  

  • Fully adaptive composition in differential privacy
    J. Whitehouse, A. Ramdas, R. Rogers, Z.S. Wu       arxiv  

  • Estimating means of bounded random variables by betting
    I. Waudby-Smith, A. Ramdas       arxiv  

  • Dimension-agnostic inference using cross U-statistics
    I. Kim, A. Ramdas       arxiv

  • Admissible anytime-valid sequential inference must rely on nonnegative martingales
    A. Ramdas, J. Ruf, M. Larsson, W. Koolen       arxiv

  • Comparing sequential forecasters
    Y.J. Choe, A. Ramdas       arxiv  

  • RiLACS: Risk-limiting audits via confidence sequences
    I. Waudby-Smith, P. Stark, A. Ramdas       EVoteID, 2021   arxiv   (Best Paper award)

  • False discovery rate control with e-values
    R. Wang, A. Ramdas       JRSSB, 2022   arxiv

  • Testing exchangeability: fork-convexity, supermartingales, and e-processes
    A. Ramdas, J. Ruf, M. Larsson, W. Koolen       Intl J of Approximate Reasoning, 2021   arxiv   proc

  • Time-uniform, nonparametric, nonasymptotic confidence sequences
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       Annals of Stat., 2021   arxiv   proc   code   talk1   tutorial

  • Time-uniform Chernoff bounds via nonnegative supermartingales
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       Prob. Surveys, 2020   arxiv   proc

  • Universal inference
    L. Wasserman, A. Ramdas, S. Balakrishnan       PNAS, 2020   arxiv   talk   proc

  • A unified treatment of multiple testing with prior knowledge using the p-filter
    A. Ramdas, R. F. Barber, M. Wainwright, M. Jordan       Annals of Stat., 2019   arxiv   code   proc

  • Classification accuracy as a proxy for two sample testing
    I. Kim*, A. Ramdas*, A. Singh, L. Wasserman       Annals of Stat., 2021   arxiv   proc

  • Predictive inference with the jackknife+
    R. Barber, E. Candes, A. Ramdas, R. Tibshirani       Annals of Stat., 2020   arxiv   code

  • Simultaneous high-probability bounds on the FDP in structured, regression and online settings
    E. Katsevich, A. Ramdas       Annals of Stat., 2020   arxiv   code   proc


Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was a postdoc at UC Berkeley (2015–2018) and obtained his PhD at CMU (2010–2015), receiving the Umesh K. Gavaskar Memorial Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay (2005-09), and he did high-frequency algorithmic trading at a hedge fund (Tower Research) from 2009-10.

Aaditya was an inaugural inductee of the COPSS Leadership Academy, and a recipient of the 2021 Bernoulli New Researcher Award. His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award (2020), an ARL Grant on Safe Reinforcement Learning, the Block Center Grant for election auditing, a Google Research Scholar award (2022) for structured uncertainty quantification, amongst others.

Aaditya's main theoretical and methodological research interests include selective and simultaneous inference (interactive, structured, online, post-hoc control of false decision rates, etc), game-theoretic statistics (sequential uncertainty quantification, confidence sequences, always-valid p-values, safe anytime-valid inference, e-processes, supermartingales, etc), and distribution-free black-box predictive inference (conformal prediction, calibration, etc). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, financial), and his group's work has received multiple best paper awards.

He is one of the organizers of the amazing and diverse StatML Group at CMU. Outside of work, some easy topics for conversation include travel/outdoors (hiking, scuba, etc.), trash-free living, completing the Ironman triathlon and long-distance bicycle rides.

Curriculum Vitae