Enoch Hyunwook Kang 

As a computer scientist with a prior Ph.D. and an econometrician, I develop methods to advance marketing decision-making using AI alignment, causal ML, online experimentation, structural econometrics, reinforcement learning theory, and AI agents.  My recent work covers a scalable machine learning method for dynamic discrete choice (invited tutorial at the Econometric Society Summer School 2025), self-improving AI as Bayesian Optimization in language space, the emergence of equilibrium in the economy of AI agents, personalized AI alignment, reinforcement learning theory, and auto-debiasing unstructured data using language model representations.  As a body of work, my research presents new methods that shed light on the inflection points the marketing industry faces in the era of AI. I also host a bi-daily "Best AI papers explained" podcast.

Computer Science methods: AI agents, AI alignment, Machine learning theory, Reinforcement learning theory, Adaptive experimentation theory, Graph neural networks

Econometric/Economic methods: Dynamic Structural Models, Causal Machine Learning,  Game theory

Ph.D. candidate, Marketing, University of Washington (Expected 2027)

Ph.D., Computer Engineering, Texas A&M University (2023)

B.S., Mathematics, Korea Advanced Institute of Science & Technology (2017)

Program Committee, ACM Economics and Computation (2026)

Reviewer, ICLR (2026, 2025, 2024, 2023)

Reviewer, ICML (2026, 2025, 2024, 2023)

Reviewer, NeurIPS (2026,2025, 2024, 2023, 2022)

Reviewer, AISTATS (2023)