Date/Time

Date(s) - 06/01/2022
4:30 pm - 5:30 pm

Please use this zoom link for the event.

 

Please join us for a UW Data Science Seminar event on Wednesday, June 1st from 4:30 to 5:30 p.m. PDT. The seminar will feature Priya L. Donti, PhD Candidate in Computer Science and Public Policy at Carnegie Mellon University, and co-founder and chair of Climate Change AI.

This talk is co-sponsored by the Environmental Impacts of Data Science Special Interest Group at the eScience Institute. The event will be held in the UW Physics and Astronomy Auditorium (PAA 118)

“Tackling Climate Change with Machine Learning”

Abstract: Climate change is one of the greatest challenges that society faces today, requiring rapid action from all corners. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change, when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will then dive into some of my own work in this area, which merges data-driven approaches with physical knowledge to facilitate the transition to low-carbon electric power grids.

Biography: Priya Donti is a Ph.D. Candidate in Computer Science and Public Policy at Carnegie Mellon University. She is also a co-founder and chair of Climate Change AI, an initiative to catalyze impactful work in climate change and machine learning. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Priya is a member of the MIT Technology Review’s 2021 list of “35 Innovators Under 35,” and is a recipient of 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.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

The 2021-2022 seminars will be both in-person and virtual, and are free and open to the public.