Hello! I am Ph.D. student in Computer Science and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. My work lies at the intersection of machine learning, electric power systems, and climate change mitigation. Specifically, I am interested in creating novel machine learning techniques that incorporate domain knowledge (such as power system physics) to reduce greenhouse gas emissions from the electricity sector. I am funded through the U.S. Department of Energy’s Computational Science Graduate Fellowship and was previously an NSF Graduate Research Fellow. I am also a co-chair of Climate Change AI and a lead organizer of the Computational Sustainability Network’s CompSust Open Graduate Seminar (COGS).
Before CMU, I was a Thomas J. Watson Fellow traveling the world to study the people, technologies, and policies behind next-generation electricity systems (project blog here). I did my undergrad at Harvey Mudd College in Computer Science/Math with an Emphasis in Environmental Analysis.
- How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM
Priya L. Donti, J. Zico Kolter, and Inês Lima Azevedo
Environmental Science & Technology (2019)
- SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder, and J. Zico Kolter
🏆 Honorable mention at the International Conference on Machine Learning (ICML) 2019
[paper] [code] [poster] [slides]
- Task-based End-to-End Model Learning in Stochastic Optimization
Priya L. Donti, Brandon Amos, and J. Zico Kolter
Neural Information Processing Systems (NIPS) 2017
[paper] [poster] [video] [code]
- Predicting the Quality of User Experiences to Improve Productivity and Wellness (poster)
Priya L. Donti, Jacob Rosenbloom, Alex Gruver, and James C. Boerkoel Jr.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
- Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti*, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio
*Co-editor of full paper, and sole author of Electricity Systems section.
[paper] [press coverage]
- Matrix Completion for Low-Observability Voltage Estimation
Priya L. Donti, Yajing Liu, Andreas J. Schmitt, Andrey Bernstein, Rui Yang, and Yingchen Zhang
- Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data
Priya L. Donti, Inês Lima Azevedo, and J. Zico Kolter
🏆 Highlighted paper at the AI for Social Good workshop at NeurIPS 2018
🏆 Best poster at the Power and Energy Conference at Illinois (PECI) 2019