Teaching#
Courses#
Geophysical Fluid Dynamics (DEES, Columbia University)#
A graduate course covering the mathematical foundations of ocean and atmosphere dynamics, emphasizing physical intuition alongside rigorous derivations. The course integrates numerical simulations into traditionally theoretical material, with in-class and homework assignments that require students to run simulations under different parameter regimes and interpret results in the context of theory.
Supplementary codes: GitHub
Computing and Research Methods for Climate Data Science (Climate School, Columbia University)#
A graduate course on scalable data analysis methods using Python, cloud computing, and modern workflows for handling the massive datasets common in Earth system science. The course emphasizes thinking critically about analytical approaches and leveraging modern tools, including large language models, in the context of climate research.
Course materials: earth-ds-ml.github.io
Introduction to Climate Modeling (Climate School, Columbia University)#
A new course introducing students to the fundamentals of climate modeling, from the underlying physical principles to practical aspects of running and interpreting climate simulations. Starting Fall 2026.
Open Educational Resources#
Learning Machine Learning with Lorenz-96#
An interactive Jupyter Book developed with the M²LInES team, designed to introduce machine learning concepts in the context of climate science using the Lorenz-96 model. Published in the Journal of Open Source Education.