October 28, 2024 6:30 pm
Town Hall Seattle, Livestream (Hybrid)
Pay What You WillAvailableRecordedComing Soon CART Captioning
![Headshot of Priya Donti](https://uw-s3-cdn.s3.us-west-2.amazonaws.com/wp-content/uploads/sites/178/2024/07/17102248/300x300_Priya-Donti.jpg)
In this talk Dr. Priya Donti, Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS, will explore AI’s multi-faceted relationship with climate change. This discussion will cover how researchers, practitioners, and policymakers can work together to better align the use of AI with climate change.
Registration opens on September 12, 2024.
About the speaker
Priya Donti
Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT
Dr. Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Methodologically, this entails exploring ways to incorporate relevant physics, hard constraints, and decision-making procedures into deep learning workflows. Priya is also the co-founder and Chair of Climate Change AI, a global nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the AI2050 Early Career Fellowship, the ACM SIGEnergy Doctoral Dissertation 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.
Sponsoring Departments: The Graduate School, Evans School of Public Policy & Governance, Program on Climate Change, Department of Atmospheric Sciences, Paul G. Allen School of Computer Science & Engineering