Prof. Priya Lekha Donti

Silverman (1968) Family Career Development Assistant Professor of Electrical Engineering and Computer Science

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 45-601F

Research Summary

Professor Donti work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning.

Recent Work

  • Video

    2024 MIT Sustainability Conference: Tackling Climate Change with Machine Learning

    October 22, 2024Conference Video Duration: 28:50

    Tackling Climate Change with Machine Learning
    Priya L. Donti
    Assistant Professor, MIT Department of Electrical Engineering and Computer Science
    Assistant Professor, MIT Laboratory for Information & Decision Systems 
    Co-founder and Chair, Climate Change AI

    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.