Enoch Hyunwook Kang

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

Econometric methods for social science: Dynamic Structural Models, Causal Machine Learning

Enoch Hyunwook Kang is a researcher studying marketing at the University of Washington. As a computer scientist with a prior Ph.D and an econometrician, he develops methods for innovating marketing-related decision-making using AI, causal ML, online experimentation, structural econometrics, reinforcement learning theory, and AI agents. His 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 alignment, and auto-debiasing unstructured data using language model representations.  As a body of work, his research presents new methods that shed light on the inflection points that the marketing industry is facing in the era of AI. He also hosts a bi-daily "Best AI papers explained" podcast.

Ph.D. candidate, Marketing, University of Washington

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

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

Reviewer, Marketing Science 

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)

> May 5, 2026: Best AI papers explained (Apple podcasts, Spotify) hit 1000 subscribers with 250+ daily listeners!

> July 15, 2025: My tutorial lecture at Econometric Society Summer School in Dynamic Structural Econometrics 2025 with John Rust is out! (YouTube video below)