I am a research scientist at Meta working on adaptive experimentation on the Central Applied Science team.

My research develops Bayesian and probabilistic methods for modeling and learning from human behavior and feedback, with the goal of making adaptive learning systems more data-efficient, interpretable, and responsive to human context. I study how uncertainty, preferences, and behavioral patterns can inform learning and optimization in both large-scale experimentation and small-sample decision settings. More broadly, I’m interested in how computational models can help us better understand human behavior and, in turn, design AI systems that interact with people in more reliable and meaningful ways. My work has been covered in the New York Times, LA Times, San Francisco Chronicle, the Verge, Washington Post, MIT Technology Review, Scientific American, and other places.

I received my Ph.D. in computer science from Stanford University advised by Sharad Goel and B.S. in computer science with a minor in probability and statistics from Georgia Institute of Technology. You can reach me at zylin@cs.stanford.edu.

Hi, hello, howdy! Since nobody calls me Dr. I guess this is still Mr. Lin here!