Casual Inference podcast

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

0:00
48:23
Retroceder 15 segundos
Avanzar 15 segundos

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.

Follow along on Twitter:

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

Otros episodios de "Casual Inference"