Teaching

Courses

EEPS 3230 Spring

Biogeochemistry

How do carbon, nitrogen, phosphorus, and sulfur move between rocks, water, and air? What happens when people speed those cycles up? We go through the major biogeochemical cycles and spend time on isotopic tracers, redox chemistry, box models, and greenhouse gas budgets. Open to EEPS students, engineers, and environmental science majors.

EEPS 4454 Fall

Organic Geochemistry

Organic molecules in soils, sediments, and petroleum carry information about the organisms and environments that made them. We look at how those compounds form, get buried, survive (or don’t), and what they can tell us about the past. Kerogen, coal, petroleum, biomarkers, and the analytical tools you’d use to study them. Prerequisites: EEPS 2022 and Chem 1601 or 1701, or instructor permission.

EEPS 4684 Fall

Geospatial Field Methods

You learn to use high-precision GPS, total stations, laser scanners, and drones with various sensors (RGB, multispectral, lidar) to make measurements in the field. It’s project-based — you figure out what to measure, go collect data, process it, and present the results. Useful well beyond EEPS; we’ve had students from archaeology, ecology, and landscape architecture.

EEPS 4960 Spring

Field Geology

Geology outside — mapping, measuring sections, identifying structures, collecting samples. The big event is an international field trip over spring break. EEPS majors and minors; requires instructor permission.

EEPS 5000 Fall

AI Inference in Geoscience

Pretrained AI models can now segment satellite imagery, pick seismic phases, classify minerals, detect faults, map craters, and forecast sea ice — often zero-shot, with no task-specific training. This project-based seminar teaches you to apply these models to real geoscience datasets, compare their output against simple baselines, and judge when AI inference actually adds value to a workflow. You’ll run models on WashU’s Compute2 GPU platform and finish with a documented, reproducible inference pipeline. A one-credit Special Topics course; no prior machine-learning experience required.

Full course descriptions are available in the WashU bulletin.