Henry Chai

Henry Chai is an Assistant Teaching Professor in the Machine Learning Department at Carnegie Mellon University (CMU). His role focuses on teaching core machine learning courses such as Introduction to Machine Learning (10-301/601) and Bayesian Methods in Machine Learning (10-424/624). He co-teaches courses with faculty like Matt Gormley and Charlie Garrod.

Chai holds a PhD from Washington University in St. Louis (2021), advised by Roman Garnett. His research explores Bayesian machine learning, probabilistic numerics, and active learning, aiming to address computational challenges in reasoning about intractable quantities. He has contributed to methods like BINOCULARS and Bayesian quadrature optimization, published in top venues like ICML and AISTATS.

He actively engages in pedagogical research and K-12 CS education advocacy. His teaching responsibilities include advanced graduate courses (e.g., 10-701) and undergraduate introductory courses, with a focus on scalable Gaussian processes and Bayesian optimization. Office hours are held in GHC 8133.