The panel discussion will focus on the future of model interpretability in the Earth and Space Sciences. We will cover topics such as where the most promising and/or urgent Earth Science applications lie, what the current state-of-the-art is with regards to model interpretability and explainability, and where the field is heading.

Our panelists:

Leilani Gilpin: Dr. Gilpin is an Assistant Professor in the Department of Computer Science and Engineering at UC Santa Cruz. Her research focuses on the design and analysis of methods for autonomous systems to explain themselves. Her work has applications to robust decision-making, system debugging, and accountability. Her current work examines how generative models can be used in iterative XAI stress testing.

Andrew Ross: Dr. Ross is a postdoctoral fellow at NYU researching how to improve ocean and climate models with hopefully-interpretable ML methods, working under Laure Zanna as part of M²LInES. Previously, he did his PhD in interpretable ML at Harvard University with Finale Doshi-Velez.

Antonios Mamalakis: Dr. Mamalakis is a postdoctoral researcher at the Department of Atmospheric Science of Colorado State University, working with professors Imme Ebert-Uphoff and Elizabeth Barnes. His research focuses on the application of machine learning (ML) and ML interpretability methods to climate problems, on climate predictability and teleconnections, climate change impacts, and hydrology. Dr Mamalakis holds a PhD in Civil and Environmental Engineering from the University of California, Irvine, and a MSc and a diploma from the University of Patras, Greece.

Moderators: Professor Karianne Bergen, Brown University Dr. Natasha Dudek, McGill University and Mila