[Colloquium] TODAY: Priya Donti (CMU) – Optimization-in-the-loop AI for energy and climate (Candidate Talk)

[Rob Mitchum]

*Data Science Institute/Computer Science/Statistics Joint Candidate Seminar*

*Priya Donti*
*Ph.D. Candidate*
*Carnegie Mellon University*

*Thursday, January 20th*
*2:00 p.m. - 3:00 p.m.*
*Live Stream <http://live.cs.uchicago.edu/priyadonti/> or Zoom
<https://uchicago.zoom.us/j/91298913771?pwd=N28xVDQ1cldFUmNqeERvQ2hjU3RJQT09>
(full details below)*


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

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. In this talk, I present the
framework of “optimization-in-the-loop” AI, and show how it can address
such challenges by enabling the design of methods that explicitly capture
relevant constraints and decision-making procedures within the learning
process. 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 their 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 critical to climate action.

*Bio:* Priya Donti <https://priyadonti.com/> 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.

*Host: *Rebecca Willett

*Zoom Info:*
https://uchicago.zoom.us/j/91298913771?pwd=N28xVDQ1cldFUmNqeERvQ2hjU3RJQT09
Meeting ID: 912 9891 3771
Passcode: ds2022
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20220120/db0c54ea/attachment.html>