Abigail Bodner

Abigail Bodner is an Assistant Professor at the Massachusetts Institute of Technology with a joint appointment between the Department of Earth, Atmospheric and Planetary Sciences in the School of Science and the Department of Electrical Engineering and Computer Science.

Her educational background includes:

• BSc in Mathematics and Earth Sciences from Tel Aviv University
• MSc in Atmospheric Sciences from Tel Aviv University
• ScM in Applied Mathematics from Brown University
• PhD in Earth, Environmental and Planetary Science from Brown University (2021)

Professor Bodner's research spans climate science, physical oceanography, geophysical fluid dynamics, and turbulence. She investigates multi-scale turbulent interactions in the upper ocean from O(100 km) to O(1 m) using a combination of theoretical approaches, high-resolution numerical simulations, climate models, and machine learning techniques. A key focus of her work is understanding the dynamic interplay between ocean submesoscales and boundary layer turbulence, with recent interest expanding to coastal sea level drivers. Her research has significant implications for improving climate models by better representing unresolved physics associated with submesoscale turbulence.

Her scientific contributions have been recognized with several prestigious awards:

• NASA Transform to Open Science Training Award (2023)
• Simons Society Junior Fellow (2021)
• Community Earth Systems Model Graduate Student Award

Professor Bodner is passionate about teaching computational tools for climate science and co-founded Climatematch Academy, an online program dedicated to building a global and diverse climate science community equipped with computational tools to address climate challenges. She is actively involved in mentoring students and has an open postdoctoral position focused on implementing data-driven approaches in ocean modeling.

Her research is conducted through collaborations with the Multiscale Machine Learning In Coupled Earth System Modeling (M²LInES) initiative, where she works on developing data-driven parameterizations for submesoscale processes in climate models.