Aaditya's research is in mathematical statistics and learning, with an eye towards designing algorithms that both have strong theoretical guarantees and also work well in practice. His main 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, post-hoc calibration, etc). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, financial, fairness), and his group's work has received multiple best paper awards.