Aaditya Ramdas

Aaditya Ramdas is an Associate Professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. He is also a visiting academic at Amazon Research. His work bridges theoretical statistics and practical machine learning, focusing on algorithms with strong guarantees for post-selection inference, game-theoretic statistics, and predictive uncertainty quantification. Applied domains include privacy, neuroscience, genetics, and auditing.

He has received prestigious awards such as the Sloan Fellowship, IMS Peter Gavin Hall Prize, and NSF CAREER Award. His research spans foundational areas like sequential testing, multiple hypothesis testing, and conformal prediction, with recent work emphasizing anytime-valid inference and robust statistical methods.

• Affiliations: Dietrich College of Humanities and Social Sciences, Amazon Research
• Labs/Teams: DELPHI Lab Group (specializing in public health forecasting), CSAFE Lab Group (computational statistics).

Key contributions include advancing e-value based methods for hypothesis testing and developing scalable techniques for uncertainty quantification in machine learning models. His work often intersects interdisciplinary fields, such as auditing AI systems for fairness and privacy compliance.