Teaching is one of the most direct ways I can contribute to shaping the next generation of Earth scientists and data practitioners. My courses span a broad audience — from hundreds of undergraduates encountering planetary science for the first time, to small graduate seminars where students build fluency in numerical methods or modern AI workflows. I aim to make quantitative thinking accessible and to model the kind of intellectual curiosity I hope students carry with them long after the semester ends.
A general education course introducing the solar system to a broad undergraduate audience — most students are not science majors. Topics include orbital mechanics, the formation of the solar system, comparative planetary geology and atmospheres, the search for life, and humanity's robotic and crewed exploration of the solar system. I emphasize quantitative reasoning throughout, connecting physical principles to what we can observe and measure. The course consistently attracts 200–290 students per semester, and I work hard to keep it engaging at that scale.
Student evaluations: ~4.5–4.6 / 5.0A graduate seminar covering the physical and mathematical foundations of fluid flow through porous media, with applications to petroleum engineering and subsurface carbon storage. Topics include Darcy's law and its extensions, multiphase flow equations, rock and fluid properties, thermodynamic phase behavior, and numerical methods for reservoir simulation. Students implement core algorithms in MATLAB throughout the semester and complete a final project involving a custom flow simulation. This course is where I connect my research most directly to the classroom — several former students have gone on to work in reservoir simulation and computational hydrology.
Student evaluations: ~4.6–4.8 / 5.0I developed this course from scratch and first taught it in Fall 2022 as ES 5194. Redesigned for Fall 2025 following inputs from the NSF Cyber2A pedagogy workshop, the course now covers the full arc of modern machine learning in Earth and environmental science: from regression and classical ML to convolutional neural networks, vision transformers, and physics-informed neural networks. Each week pairs lecture with a hands-on Python lab using real Earth science datasets — satellite imagery, geophysical logs, and climate data. Students complete a final independent AI project and present their results to the group. The course has become a foundation for graduate students across Earth Sciences, Geography, and related programs who want to incorporate AI into their research.
Student evaluations: 4.8–5.0 / 5.0