Courses in uncertainty quantification, scientific machine learning, experimental design, and computational engineering.
Graduate special-topics course on physics-informed learning, operator learning, neural differential equations, diffusion models, and hybrid scientific-model/AI approaches.
Winter 2027A graduate course connecting stochastic modelling, Bayesian inference, uncertainty quantification, and computational implementation.
Fall 2023–2026A technical elective built around experimental design, Python-based computational exercises, and an open-ended final project.
2023–2026Required second-year course in numerical computing and engineering design.
Winter 2025–2026Computation is most useful when students understand both what a method does and what assumptions make its answer credible.
Courses combine mathematical foundations, practical implementation, reproducible computational work, and open-ended projects with written and oral communication.