Priya Donti: Optimization-in-the-loop ML for energy and climate

Addressing climate change will require concerted action across society, including the development of innovative technologies. While methods from machine learning (ML) have the potential to play an important role, these methods often struggle to contend with the physics, hard constraints, and complex decision-making processes that are inherent to many climate and energy problems. 

To address these limitations, I present the framework of “optimization-in-the-loop ML,” and show how it can enable the design of ML models that explicitly capture relevant constraints and decision-making processes. For instance, this framework can be used to design learning-based controllers that provably enforce the stability criteria or operational constraints associated with the systems in which they operate. It can also enable the design of task-based learning procedures that are cognizant of the downstream decision-making processes for which a model’s outputs will be used. 

By significantly improving performance and preventing critical failures, such techniques can unlock the potential of ML for operating low-carbon power grids, improving energy efficiency in buildings, and addressing other high-impact problems of relevance to climate action.

Priya Donti is an Assistant Professor at MIT EECS and LIDS, whose 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 workflows. 

Priya is also the co-founder of Climate Change AI, a global non-profit 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 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.