He regularly publishes at top journals like The Annals of Statistics, Biometrika and IEEE Transactions on Information Theory, including prestigious discussion papers at the Journal of the Royal Statistical Society and Journal of the American Statistical Association, and at the top AI conferences like NeurIPS and ICML, including over a dozen orals/spotlights. He has given several keynote talks, including at CUSO, Lunteren, AISTATS and VMCF.

Aaditya's research in mathematical statistics and learning has an eye towards designing algorithms that both have strong theoretical guarantees and also work well in practice. His main interests include post-selection inference (multiple testing, simultaneous inference), game-theoretic statistics (e-values, confidence sequences) and predictive uncertainty quantification (conformal prediction, calibration). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, finance, fairness).