Biography
Nisheeth Vishnoi’s work spans various areas of mathematics, theoretical computer science, optimization, and artificial intelligence. He aims to tackle some of the most pressing and complex problems at the intersection of computation and society.
Vishnoi studies foundational questions about algorithmic fairness, privacy, and decision-making, especially in settings where algorithms interact with human judgment, institutional processes, and social norms. His work includes models of bias and strategic behavior in selection systems, as well as the design of equitable and private mechanisms. He also develops mathematical tools for efficient learning in diffusion models, particularly in geometrically structured spaces. More recently, Vishnoi has been building theoretical frameworks to understand the impact of AI — such as large language models — on work, science, knowledge, and societal systems. This includes examining how AI alters skill formation, decision structures, and human-AI collaboration and how we might build more accountable, interpretable, and humane computational systems in response.
At Yale, Vishnoi co-founded the Computation and Society Initiative. He is co-PI of an NSF-funded AI Institute: The Institute for Learning-enabled Optimization at Scale. In addition, he is affiliated with the Cowles Foundation for Research in Economics, the Institution for Social and Policy Studies, and the Thurman Arnold Project at the Yale School of Management.