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 research lies at the intersection of machine learning and electric power systems, using algorithmic and policy analytic approaches to promote efficient and low-emission operation of the energy grid. I am funded through the U.S. Department of Energy’s Computational Science Graduate Fellowship and was previously an NSF Graduate Research Fellow.
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.
- 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)
- 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