## Aaditya Ramdas
Peter Grünwald and I are jointly editing a special issue on game-theoretic statistics and safe anytime-valid inference (click here), due March 2023. Please consider submitting your latest work if you work in the area. Do read our brief but broad survey on the topic. ## These keywords quickly get my attention**e-values**(confidence sequences, e-processes, supermartingales, testing by betting, sequential inference, optional stopping, peeking and p-hacking, change detection, anytime p-values, Ville's inequality, game-theoretic statistics)**conformal prediction and calibration**(distribution-free inference, uncertainty quantification for black-box machine learning, covariate/label shift, beyond exchangeability)**multiple hypothesis 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. ## Selected recent papers (for all papers, see this page (by topic) or my CV (by year))**Game-theoretic statistics and safe anytime-valid inference**
*A. Ramdas, P. Grunwald, V. Vovk, G. Shafer*arxiv
**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*`JRSSB, 2023`arxiv (Discussion paper)
**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
**RiLACS: Risk-limiting audits via confidence sequences**
*I. Waudby-Smith, P. Stark, A. Ramdas*`EVoteID, 2021`arxiv (Best Paper award)
**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
**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
## BiographyAaditya 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 recipient of the COPSS Leadership Award (2021), and a recipient of the Bernoulli New Researcher Award (2021). His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award (2019), an ARL Grant on Safe Reinforcement Learning, a Block Center Grant for election auditing, a Google Research Scholar award (2022) for structured uncertainty quantification, amongst others. He was a CUSO lecturer (2022) and will be a Lunteren lecturer in 2023. Aaditya's main theoretical and methodological research interests include 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. |