Björn Lütjens
Björn Lütjens uses machine learning (ML) to tackle climate change, little-by-little. In particular, he is concerned about the computational complexity of climate models. As a solution, he is developing hybrid ML-physics models as fast approximations, or ’emulators’, of climate models. He is currently a postdoctoral associate in the MIT Dept. of Earth, Atmospheric, and Planetary Sciences with Raffaele Ferrari and Noelle Selin. He has worked in ML and climate at IBM, John Deere, and NASA FDL. He has earned his PhD with Dava Newman at MIT in ML for Earth System Modeling, his M.Sc. with Jon How from MIT in robust deep reinforcement learning and his B.Sc. from Technical University of Munich in engineering science. He also windsurfs poorly, switches hobbies every year, and likes learning new languages. For more details see his webpage.
