Henry Chai

Hi there! I am an assistant teaching professor in the machine learning department at Carnegie Mellon University. I primarily teach the department's various Introduction to Machine Learning courses. In addition, I am passionate about pedagogical research and K-12 computer science education.

Previously, I was a postdoctoral teaching fellow in the machine learning department at Carnegie Mellon University. I graduated with my PhD from Washington University in St. Louis in 2021, where I was advised by Roman Garnett. My research interests lie at the intersection of Bayesian machine learning, probabilistic numerics and active learning. They can be concisely summarized by the following question: how can we efficiently and accurately reason about inherently intractable quantities?

My most up-to-date CV can be found here.

Current Courses

• Spring 2025: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley
• Spring 2025: (CMU) 10-424/624 Bayesian Methods in Machine Learning
• Spring 2025: (CMU) 07-070 Teaching Techniques for Computer Science, co-taught with Charlie Garrod and Kelly Rivers

Previous Courses

• Fall 2024: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley
• Fall 2024: (CMU) 10-423/623 Generative AI, co-taught with Matt Gormley
• Summer 2024: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's)
• Summer 2024: (CMU) 10-680 Mathematical Foundations for Machine Learning
• Summer 2024: (CMU) 10-681 Computational Foundations for Machine Learning
• Spring 2024: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley and Hoda Heidari
• Spring 2024: (CMU) 10-701 Introduction to Machine Learning (PhD)
• Fall 2023: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley
• Fall 2023: (CMU) 10-701 Introduction to Machine Learning (PhD), co-taught with Zack Lipton
• Summer 2023: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's)
• Fall 2022: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley
• Fall 2022: (CMU) 10-605/805 Machine Learning with Large Datasets, co-taught with Ameet Talwalkar
• Summer 2022: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's)
• Spring 2022: (CMU) 10-315 Introduction to Machine Learning (SCS Majors), co-taught with Aarti Singh
• Spring 2022: (CMU) 10-701 Introduction to Machine Learning (PhD), co-taught with Nina Balcan
• Fall 2021: (CMU) 10-701 Introduction to Machine Learning (PhD), co-taught with Pat Virtue and Ziv Bar-Joseph
• Fall 2021: (CMU) 10-301/601 Introduction to Machine Learning (Undergraduate/Master's), co-taught with Matt Gormley
• Spring 2021: (WUSTL) CSE 515T Bayesian Methods in Machine Learning
• Fall 2019: (WUSTL) CSE 417T Introduction to Machine Learning
• Fall 2018: (WUSTL) CSE 417T Introduction to Machine Learning, co-taught with Chien-Ju Ho

Research

• S. Jiang, H. Chai, J. Gonzalez and R. Garnett. BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design. ICML 2020 (conference talk available here).
• H. Chai, J.-F. Ton, M.A. Osborne and R. Garnett. Automated Model Selection with Bayesian Quadrature. ICML 2019.
• H. Chai and R. Garnett. Improving Quadrature for Constrained Integrands. AISTATS 2019.