Our weekly SRI Seminar Series welcomes Priya L. Donti, co-founder and executive director of Climate Change AI, and an incoming assistant professor at MIT’s Department of Computer Science and Electrical Engineering. Donti was listed as one of “35 Innovators Under 35” by MIT Technology Review in 2021, and is a former a U.S. Department of Energy Computational Science Graduate Fellow, a Siebel Scholar, an NSF Graduate Research Fellow, and a Thomas J. Watson Fellow. She received her PhD in computer science and public policy from Carnegie Mellon University.
Donti’s research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids, exploring methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. In this talk, she will discuss how machine learning can be used to address the urgent problem of climate change, in coordination with policy and engineering initiatives, through methods such as distilling decision-relevant information, optimizing complex systems, and accelerating the pace of scientific experimentation and discovery.
Talk title:
“Tackling climate change with machine learning”
Abstract:
Climate change is one of the greatest challenges that society faces today, requiring rapid action from across society. 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 also describe key considerations for the responsible development and deployment of such work. While this talk will primarily discuss opportunities for machine learning to help address climate change, it is worth noting that machine learning is a general-purpose technology that can be used for applications that both help and hinder climate action. In addition, machine learning has its own computational and hardware footprint. I will therefore briefly present a framework for understanding and contextualizing machine learning’s overall climate impacts, and describe associated considerations for machine learning research and practice as a whole.
Recommended readings:
D. Rolnick, P. L. Donti, et al., “Tackling climate change with machine learning,” ACM Computing Surveys 55(2) March 2023, 1–96.
L. H, Kaack, P. L. Donti, et al., “Aligning artificial intelligence with climate change mitigation,” Nature Climate Change 12 June 2022, 518–527.
About Priya Donti
Priya L. Donti is the co-founder and executive director of Climate Change AI, a non-profit initiative to catalyze impactful work at the intersection of climate change and machine learning, which she is currently running through the Cornell Tech Runway Startup Postdoc Program. She will also join MIT EECS as an assistant professor in Fall 2023. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her work explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models.
Donti received her PhD in computer science and public policy from Carnegie Mellon University, and is a recipient of MIT Technology Review’s 2021 “35 Innovators Under 35” award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.
About the SRI Seminar Series
The SRI Seminar Series brings together the Schwartz Reisman community and beyond for a robust exchange of ideas that advance scholarship at the intersection of technology and society. Seminars are led by a leading or emerging scholar and feature extensive discussion.
Each week, a featured speaker will present for 45 minutes, followed by an open discussion. Registered attendees will be emailed a Zoom link approximately one hour before the event begins. The event will be recorded and posted online.
Priya L. Donti