[Theory] REMINDER: 2/24 Talks at TTIC: Priya Donti, Carnegie Mellon University

[Mary Marre]

*When:*        Thursday, February 24th at* 11:00 am CT*


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Where:*       Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN__Uhhgg62RFqprgfs3-XOZg>*)


*Who: *         Priya Donti, Carnegie Mellon University



Title: Optimization-in-the-loop AI for Energy and Climate

Abstract: 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 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:* *David McAllester <mcallester at ttic.edu>*





Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Fri, Feb 18, 2022 at 11:32 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Thursday, February 24th at* 11:00 am CT*
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Where:*       Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN__Uhhgg62RFqprgfs3-XOZg>*
> )
>
>
> *Who: *         Priya Donti, Carnegie Mellon University
>
>
>
> Title: Optimization-in-the-loop AI for Energy and Climate
>
> Abstract: 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 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:* *David McAllester <mcallester at ttic.edu>*
>
>
>
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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