I am a 5th year graduate research assistant at UC Irvine’s Department of Earth System Science. My PhD work applies machine learning to emulate subgrid physical processes in multiscale climate models in the hopes enabling high fidelity ensemble climate projections without waiting for decades of continued Moore’s Law. My research interests extend to uncertainty quantification for data-driven methods in physics-based climate models and embedding physical priors and constraints when learning from data.

I believe machine learning is uniquely suited for positive societal impact in climate science because of access to large volumes of data, an explosion of research and interest in machine learning / AI generally, and the human desire to leave the world in a better place than we found it. If you are a machine learner OR a climate science domain expert looking to collaborate, please do not hesitate to contact me at jerryL9 at uci.edu.

more about me

Outside of immediate research interests, I am broadly interested in the protection of democratic institutions and ways technology can be oriented towards (and not against) societal progress. I also enjoy running, ebiking, board games, and learning new things.

For further inquiries, you can contact me at jerryL9 at uci.edu. You can also find me on Bluesky at jlin96.bsky.social.