Priya Donti creates algorithms that help electricity networks handle wind and solar energy more efficiently.
As a profile by MIT News explained, Donti spent her childhood in Massachusetts but traveled regularly to India to visit relatives.
The contrast between the two nations revealed stark global inequalities. Later, a high school teacher showed her class how rising global temperatures amplify existing inequalities.
She enrolled at Harvey Mudd College intending to study materials science and chemistry, hoping to improve solar cells, but computer science courses shifted her focus.
She found work from U.K.-based scientists showing how machine learning could help electrical infrastructure manage renewable sources, and the discovery united her interests.
While completing doctoral work at Carnegie Mellon, Donti started Climate Change AI. The nonprofit connects scientists, policy experts, and people implementing solutions to environmental problems, providing access to shared resources.
Donti's algorithms address a challenge power companies face: renewable sources don't produce a steady output the way coal or gas plants do.
Her models account for real-world physical constraints and scientific principles governing electrical systems to improve how operators forecast production and manage their networks.
She developed one algorithm that operates faster than existing methods while reducing expenses. It helps power companies cut costs while respecting genuine physical boundaries instead of rough estimates. The technology has drawn interest from electrical network operators.
Donti also creates artificial datasets for other researchers. Real information about power infrastructure usually stays confidential for competitive or security reasons, but her synthetic data gives scientists material to develop better management tools without accessing restricted information.
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Yes 👍 Improved network management means lower power bills for households. It also lets more clean sources replace polluting generators.
Donti teaches at MIT and will lead a spring course about applying AI to environmental challenges. She selected the school for its culture.
"I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society," she said.
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