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Kawin Ethayarajh works on behavioral machine learning, an emerging field at the intersection of artificial intelligence, economics, and behavioral science. His research challenges traditional machine learning methodologies by emphasizing the importance of real-world actors such as consumers, firms, and states. By borrowing concepts from economics, he formalizes behavior to create algorithms, tools, and platforms that are compatible with actual behavioral agents rather than idealized models. Ethayarajh is known for creating the Stanford Human Preferences (SHP) dataset, the largest repository of human preferences text, and the Kahneman-Tversky Optimization (KTO) algorithm, which aligns language models with user feedback. His work has garnered significant recognition, including the Meta Fellowship in 2021 and the ICML Outstanding Paper Award in 2022, with his models achieving hundreds of millions of downloads. Before joining the University of Chicago Booth School of Business, Ethayarajh was a postdoctoral fellow at the Princeton Language Intelligence group. He holds a Ph.D. in Computer Science from Stanford University and a B.Sc. in Computer Science from the University of Toronto.
The doctoral program at Booth is organized into 'dissertation areas' which include Accounting, Behavioral Science, Econometrics and Statistics, Finance, Marketing, and Operations Management.