Nitin Kohli is a PhD student at the UC Berkeley School of Information, researching topics that span privacy, security, and fairness. Utilizing techniques from game theory, mechanism design, cryptography, and statistics, Nitin develops theory and tools that safeguard sensitive data by constructing algorithmic mechanisms with provable guarantees over the outcomes of their use. Additionally, he also explores legal and policy mechanisms to protect these values by analyzing the incentive structures, power dynamics, and adversarial opportunities that govern these environments.
Prior to his PhD, Nitin worked both as a data scientist in industry and as an academic. Within industry, Nitin developed machine learning and natural language processing algorithms to identify occurrences and locations of future risk in healthcare settings. Within academia, Nitin worked as an adjunct instructor and lecturer at UC Berkeley, teaching both introductory and advanced courses in elementary mathematics, probability, statistics, and game theory. Nitin holds a Master’s Degree in Information and Data Science from Berkeley’s School of Information, and a Bachelor’s Degree in Mathematics and Statistics, where he received departmental honors in statistics for his work in stochastic modeling and game theory.
- Data Privacy: Foundations and Applications, Spring 2019. Visiting Graduate Student.