Nitin researches topics that span privacy and fairness. Drawing upon the combination of technical, legal, and social science scholarship, Nitin develops theory, tools, and frameworks that safeguard individuals while attending to the social and political context of their use. In particular, Nitin utilizes techniques from applied mathematics -- such as game theory and mechanism design, cryptography, statistics, and the theory of computation -- to not only construct statistical and algorithmic mechanisms with provable guarantees over the outcomes of their use, but to also show the inherent limitations present in certain technologies that are deployed in particular contexts.
Prior to his PhD, Nitin worked as a data scientist in industry, as well as an adjunct instructor and lead instructor / lecturer at UC Berkeley, teaching both introductory and advanced courses in elementary mathematics, probability, statistics, and game theory. In 2019, Nitin was awarded both the Outstanding Graduate Student Instructor Award and the Teaching Effectiveness Award for his work in Info 188 (Behind the Data: Humans and Values) under Professor Deirdre Mulligan. Of late, Nitin has been teaching courses relating to statistics, differential privacy, information law and policy, and the social and political implications of data science.
Nitin holds a master's degree in information and data science from UC Berkeley's School of Information. Nitin also holds a bachelor's degree in mathematics and statistics from UC Berkeley, where he received departmental honors in statistics.