I am a research scientist in Meta's Applied AI organization, where I work on data-centric model improvement for coding agents and AI systems. My research develops Bayesian and probabilistic methods for human-aligned, sample-efficient learning and optimization from scarce human judgment, preferences, behavioral signals, and uncertain outcomes. Previously, I was on Meta's Adaptive Experimentation team in Central Applied Science (fka Core Data Science), where I led work modernizing adaptive experimentation and Bayesian optimization, connecting preference learning, language feedback, and agentic optimization methods with applications in product, infrastructure, recommender, and AI systems. I am also a core contributor to the open-source projects BoTorch and Ax.

Across this work, I study how uncertainty, expert judgment, natural-language feedback, and behavioral signals can inform active data collection, preference learning, model evaluation, and adaptive optimization in AI systems 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 learn from and 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.