ML Seminar: Priya L. Donti

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the Spring 2023 semester.

This week's speaker, Priya L. Donti (Climate Change AI), will be giving a talk titled "Tackling Climate Change with Machine Learning".

Abstract

Climate change is one of the greatest challenges that society faces today, requiring rapid action from all corners. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change, when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high-impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will then dive into some of my own work in this area, which merges data-driven approaches with physical knowledge to facilitate the transition to low-carbon electric power grids. Specifically, I will present a framework called “optimization-in-the-loop machine learning,” and show how it can enable the design of machine learning models that explicitly capture relevant constraints and decision-making processes that are critical to enforce in power grids. I will end by presenting important considerations for developing and deploying work in this area, as well as routes to get involved.

Biography

Priya L. Donti is the Co-founder and Executive Director of Climate Change AI (CCAI), a global non-profit initiative to catalyze impactful work at the intersection of climate change and machine learning. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, my work explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning workflows.