Teaching
“Outsiders see mathematics as a cold, formal, logical, mechanical, monolithic process of sheer intellection; we argue that insofar as it is successful, mathematics is a social, informal, intuitive, organic, human process, a community project.” ~ DeMillo, Lipton and Perlis.

Modeling Earth
Most problems in Earth Science are dazzling and beautifully complex, entailing enormous complexity and spanning a wide range of spatial and temporal scales. Abstracting from this natural complexity to identify the essential components and mechanisms of a natural system is perhaps the most important, but commonly overlooked, task in mathematical modeling for Earth and Environmental Science. This course focuses on conceptual model development for natural and environmental problems specifically, rather than addressing the variety of formal mathematical techniques available for the analytical analysis or numerical simulation of a model.

Shaping the Future of the Bay Area
The complex urban problems affecting quality of life in the Bay Area, from housing affordability and transportation congestion to economic vitality and social justice, are already perceived by many to be intractable, and will likely be exacerbated by climate change and other emerging environmental and technological forces. This course sequence is designed to immerse students in the process of changing urban systems to improve the equity, resilience, and sustainability of communities by blending service learning and scientific co-production through the Stanford Future Bay Initiative.

Numerical Methods in Engineering and Applied Sciences
This course provides an introduction to numerical analysis. With the knowledge gained in this course, students will be able to design numerical methods for simulating systems of ordinary and partial differential equations. The course material is supported by engineering examples presented in the lectures, and consolidated by weekly workshops and biweekly assignments. Assignments, quizzes and the project cover both application of new algorithms and the mathematical theory on which they are based.