Teaching & Mentorship
Courses, supervision, and a teaching philosophy centered on rigor, curiosity, and real-world geoscience problems.
Teaching Philosophy
I teach AI in geophysics by linking theory to usable workflows. Students should leave with mathematical intuition, practical coding habits, and the confidence to question models rather than simply run them.
Think from first principles
Build intuition for signals, physics, and uncertainty before treating machine learning as a black box.
Learn by building
Use guided projects and reproducible notebooks so abstract ideas become applied research skills.
Connect research to society
Frame technical work around groundwater, geohazards, and environmental decision-making.
Course Offerings
AI for Geophysics
Undergraduate and Master'sAn introduction to machine learning methods for geophysical interpretation, feature engineering, inversion support, and responsible model evaluation.
Typical topics
- Supervised learning for geoscience data
- Signal preprocessing and feature design
- Model evaluation, uncertainty, and pitfalls
Time-Series Forecasting for Earth Systems
Advanced / ResearchA deeper course on forecasting, uncertainty, and physics-guided learning for environmental and subsurface systems.
Typical topics
- Sequence models and attention mechanisms
- Probabilistic forecasting and calibration
- Physics-informed constraints and transferability
Research Mentorship
I mentor students through the full research cycle: defining a question, building the workflow, evaluating evidence, and communicating results clearly.
"Good mentorship does not remove difficulty; it helps students grow strong enough to handle it."
