Jackson School of Geosciences to add undergraduate AI classes as two new faculty join

April 8, 2026

The Jackson School of Geosciences is planning a set of undergraduate classes in artificial intelligence as part of a broader effort to embed AI across its programs. Two newly hired assistant professors in the Department of Earth and Planetary Sciences, Dapeng Feng and Fa Li, will lead the undergraduate instruction beginning this fall.

Fa Li will offer a course that covers the Python programming language and core machine learning methods, aimed at undergraduates entering computational work. Dapeng Feng will take responsibility for an existing course focused on geoscience data, reshaping it to highlight AI tools used to analyze earth system information. Both faculty members bring research programs that apply AI directly to environmental questions.

Feng concentrates on hydrology and components of the water cycle, with applications that include flood and drought monitoring and managing water resources. Li studies terrestrial environments and their responses to climate change, using computational approaches to track and predict landscape change. Each employs a hybrid approach, integrating physical process models with machine learning to improve interpretations of large data sets.

Interim dean Danny Stockli says the school wants research results to move quickly into practical use, so students and stakeholders can access timely geoscience information. He highlighted existing infrastructure at the university, including a graduate stackable certificate in machine learning and data science, as a foundation for the new undergraduate offerings.

Feng and Li are preparing graduate-level classes that reflect their research specialties, creating a pipeline from introductory undergraduate training to advanced study. The Jackson School is coordinating with the College of Natural Sciences and the Oden Institute for Computational Engineering and Sciences on these curricular changes, and the two units are recruiting for a joint professor chair in computational geosciences.

In May the university will host HydroML, a symposium on machine learning in hydrology that Feng is co-chairing, an event intended to bring outside experts to campus and broaden collaboration. Faculty leaders describe the symposium and the new courses as resources that will expand hands-on opportunities for students and strengthen the school’s role in applying AI to urgent environmental challenges.

Faculty officials say the fall classes aim to introduce undergraduates to practical AI techniques used in geoscience research, and to encourage more students to pursue computational approaches in the field. The additions reflect a wider trend in geosciences, where combining traditional physical models with machine learning helps researchers handle increasingly large and complex data sets.