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SUMMARY:Optimization-in-the-loop AI for energy and climate - Priya Donti\,
  Climate Change AI\, MIT
DTSTART:20221028T120000Z
DTEND:20221028T130000Z
UID:TALK183266@talks.cam.ac.uk
CONTACT:Madeline Lisaius
DESCRIPTION:Addressing climate change will require concerted action across
  society\, including the development of innovative technologies. While met
 hods from artificial intelligence (AI) and machine learning (ML) have the 
 potential to play an important role\, these methods often struggle to cont
 end with the physics\, hard constraints\, and complex decision-making proc
 esses that are inherent to many climate and energy problems. To address th
 ese limitations\, I present the framework of “optimization-in-the-loop A
 I\,” 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 prov
 ably enforce the stability criteria or operational constraints associated 
 with the systems in which they operate. It can also enable the design of t
 ask-based learning procedures that are cognizant of the downstream decisio
 n-making processes for which a model’s outputs will be used. By signific
 antly improving performance and preventing critical failures\, such techni
 ques can unlock the potential of AI and ML for operating low-carbon power 
 grids\, improving energy efficiency in buildings\, and addressing other hi
 gh-impact problems of relevance to climate action.\n\nPriya Donti is the C
 o-founder and Executive Director of Climate Change AI\, a non-profit initi
 ative to catalyze impactful work at the intersection of climate change and
  machine learning\, which she is currently running through the Cornell Tec
 h Runway Startup Postdoc Program. She will also join MIT EECS as an Assist
 ant Professor in Fall 2023. Her research focuses on machine learning for f
 orecasting\, optimization\, and control in high-renewables power grids. Sp
 ecifically\, her work explores methods to incorporate the physics and hard
  constraints associated with electric power systems into deep learning mod
 els. Priya received her Ph.D. in Computer Science and Public Policy from C
 arnegie Mellon University\, and is a recipient of the MIT Technology Revie
 w’s 2021 “35 Innovators Under 35” award\, the Siebel Scholarship\, t
 he U.S. Department of Energy Computational Science Graduate Fellowship\, a
 nd best paper awards at ICML (honorable mention)\, ACM e-Energy (runner-up
 )\, PECI\, the Duke Energy Data Analytics Symposium\, and the NeurIPS work
 shop on AI for Social Good.
LOCATION:Seminar time is 1pm BST in Room FW 11\, Willam Gates Hall. Zoom l
 ink: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUU
 xY3Q4QT09&amp\;from=addon 
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