Colloquium Details

Optimization-in-the-loop AI for energy and climate

Speaker: Priya Donti, Carnegie Mellon University

Location: 60 Fifth Avenue 150

Date: March 24, 2022, 2 p.m.

Host: Joan Bruna


Addressing climate change will require concerted action across
society, including the development of innovative technologies. While
methods from artificial intelligence (AI) and machine learning (ML) have
the potential to play an important role, these methods often struggle to
contend with the physics, hard constraints, and complex decision-making
processes that are inherent to many climate and energy problems. To
address these limitations, I present the framework of
“optimization-in-the-loop AI,” and show how it can enable the design of
AI models that explicitly capture relevant constraints and
decision-making processes. For instance, this framework can be used to
design learning-based controllers that provably enforce the stability
criteria or operational constraints associated with the systems in which
they operate. It can also enable the design of task-based learning
procedures that are cognizant of the downstream decision-making
processes for which a model’s outputs will be used. By significantly
improving performance and preventing critical failures, such techniques
can unlock the potential of AI and ML for operating low-carbon power
grids, improving energy efficiency in buildings, and addressing other
high-impact problems of relevance to climate action.
Bio: Priya Donti is a Ph.D. Candidate in Computer Science and Public
Policy at Carnegie Mellon University. Her research explores methods to
incorporate physics and hard constraints into deep learning models, in
order to enable their use for forecasting, optimization, and control in
high-renewables power grids. She is also a co-founder and chair of
Climate Change AI, an initiative to catalyze impactful work in climate
change and machine learning. Priya is a recipient of the MIT Technology
Review’s 2021 “35 Innovators Under 35” award, the Siebel Scholarship,
the U.S. Department of Energy Computational Science Graduate Fellowship,
and best paper awards at ICML (honorable mention), ACM e-Energy
(runner-up), PECI, the Duke Energy Data Analytics Symposium, and the
NeurIPS workshop on AI for Social Good.


In-person attendance only available to those with active NYU ID cards.

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