Jason Terry

My research focuses on the physical and chemical evolution of protoplanetary disks and the origins of planetary materials. Using numerical simulations, I investigate disk instabilities, planet–disk interactions, environmental perturbations, magnetohydrodynamics, and the evolution and transport of solids and volatile species. I aim to connect these physical models with astronomical observations and the meteoritic record to better constrain the processes governing planetary system formation and evolution.

I also develop machine-learning methods for analyzing astronomical observations, including identifying signatures of embedded planets, denoising interferometric data, and detecting anomalous structures and kinematics.

My teaching experience spans introductory astronomy, observational and theoretical astrophysics, and machine learning in the physical sciences. I have designed and delivered courses and seminars, taught undergraduate tutorials, supervised undergraduate and graduate student research, and repeatedly served as a mentor for ML4SCI during the Google Summer of Code.