Dae Woong Ham

Dae Woong (David) Ham is an Assistant Professor in the Department of Technology and Operations at the University of Michigan Ross School of Business. He holds a Ph.D. in Statistics from Harvard University (2024) and a B.A. from the University of California, Berkeley (2019). His research focuses on causal inference methodologies and their applications in business and social sciences, particularly in adaptive experiments, sequential inference, and statistical gaps in tech company experimentation.

• Education:
• Ph.D. in Statistics, Harvard University, 2024
• B.A., University of California, Berkeley, 2019

His work bridges statistical theory and practical challenges, collaborating with companies like Netflix on experimentation frameworks and addressing methodological gaps in Difference-in-Difference, matching, and Fisher-Based Testing. He emphasizes design-based causal inference and non-parametric approaches, contributing to both applied and theoretical advancements in the field.

Labs/Teams: Engaged in interdisciplinary research teams at Ross, focusing on causal inference applications and tech industry partnerships.