Fun and Game(s) Theory with Aaditya Ramdas | Season 5 Episode 6

Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and uncertainty quantification for machine learning (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation. Aaditya's website: https://www.stat.cmu.edu/~aramdas/

Game theoretic statistics resources Aaditya's course, Game-theoretic probability, statistics, and learning: https://www.stat.cmu.edu/~aramdas/gtpsl/index.html

Papers of interest: Time-uniform central limit theory and asymptotic confidence sequences: https://arxiv.org/abs/2103.06476

Game-theoretic statistics and safe anytime-valid inference: https://arxiv.org/abs/2210.01948

Discussion papers: Safe Testing: https://arxiv.org/abs/1906.07801

Testing by Betting: https://academic.oup.com/jrsssa/article/184/2/407/7056412

Estimating means of bounded random variables by betting: https://academic.oup.com/jrsssb/article/86/1/1/7043257

Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi

Ellie: @EpiEllie

Lucy: @LucyStats

🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp