Priya L. Donti
Priya L. Donti
Assistant Professor, MIT EECS & LIDS; Co-founder and Chair, Climate Change AI
Verified email at - Homepage
Cited by
Cited by
Tackling Climate Change with Machine Learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022
Task-based end-to-end model learning in stochastic optimization
P Donti, B Amos, JZ Kolter
Advances in Neural Information Processing Systems, 5484-5494, 2017
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
PW Wang, PL Donti, B Wilder, Z Kolter
International Conference on Machine Learning, 6545-6554, 2019
Aligning artificial intelligence with climate change mitigation
LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick
Nature Climate Change 12 (6), 518-527, 2022
DC3: A learning method for optimization with hard constraints
PL Donti, D Rolnick, JZ Kolter
International Conference on Learning Representations, 2021
Matrix Completion for Low-Observability Voltage Estimation
PL Donti, Y Liu, AJ Schmitt, A Bernstein, R Yang, Y Zhang
IEEE Transactions on Smart Grid 11 (3), 2520 - 2530, 2019
Enforcing robust control guarantees within neural network policies
PL Donti, M Roderick, M Fazlyab, JZ Kolter
International Conference on Learning Representations, 2021
Machine Learning for Sustainable Energy Systems
PL Donti, JZ Kolter
Annual Review of Environment and Resources 46, 719-747, 2021
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization
B Chen, PL Donti, K Baker, JZ Kolter, M Berges
ACM International Conference on Future Energy Systems (ACM e-Energy), 2021
Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals
LH Kaack, PL Donti, E Strubell, D Rolnick
Heinrich Böll Foundation E-Paper, 2020
How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM
PL Donti, JZ Kolter, IL Azevedo
Environmental Science & Technology 53 (16), 9905-9914, 2019
Climate Change and AI. Recommendations for Government Action
P Clutton-Brock, D Rolnick, PL Donti, L Kaack
GPAI, Climate Change AI, Centre for AI & Climate, 2021
Adversarially robust learning for security-constrained optimal power flow
PL Donti, A Agarwal, NV Bedmutha, L Pileggi, JZ Kolter
Advances in Neural Information Processing Systems 34, 2021
Digitizing a sustainable future
LA Reisch, L Joppa, P Howson, A Gil, P Alevizou, N Michaelidou, ...
One Earth 4 (6), 768-771, 2021
A Call for Universities to Develop Requirements for Community Engagement in AI Research
E Black, J Williams, MA Madaio, PL Donti
Fair & Responsible AI Workshop @ CHI2020, 2020
Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data
PL Donti, IL Azevedo, JZ Kolter
AI for Social Good Workshop at NeurIPS 2018, 2018
How machine learning can help tackle climate change
P Donti
XRDS: Crossroads, The ACM Magazine for Students 27 (2), 58-61, 2020
Employing adversarial robustness techniques for large-scale stochastic optimal power flow
A Agarwal, PL Donti, JZ Kolter, L Pileggi
Electric Power Systems Research 212, 108497, 2022
Predicting the Quality of User Experiences to Improve Productivity and Wellness.
PL Donti, J Rosenbloom, A Gruver, JC Boerkoel Jr
AAAI, 4154-4155, 2015
Forecasting Marginal Emissions Factors in PJM
A Wang, PL Donti
Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020, 2020
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