Aaditya Ramdas
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.
Personal webpage