Please join CELI for an energy education webinar: Harnessing Artificial Intelligence to Fight Climate Change
This talk aims to provide an overview of where machine learning (ML) can be applied with high impact to address climate change, through either effective engineering or innovative research. It looks at strategies for both mitigation (reducing greenhouse gas emissions) and adaptation (preparing for unavoidable consequences). It will introduce several overarching ways in which ML can be helpful, including gathering data through remote sensing, accelerating scientific discovery, optimizing systems to improve efficiency, and speeding up physical simulations. Many of the applications discussed are implementable today, whereas others highlight cutting-edge areas of ML (such as interpretability, causality, uncertainty quantification, and physics-constrained ML techniques). The talk will also take a deeper dive into applications relevant to energy and climate change mitigation policy.
Presenter Biographies:
Lynn Kaack is a Postdoctoral Researcher in the Energy Politics Group at ETH Zurich. Her research addresses energy systems and climate mitigation policy, using methods from policy analysis, statistics, and machine learning. Currently, she leads several research projects leveraging computerized text analysis to draw insights from patent claims, climate-related financial disclosures, and renewable energy policies. She also chairs Climate Change AI, an initiative to support work at the intersection of machine learning and climate change.
Priya Donti is a PhD student in Computer Science and Public Policy at Carnegie Mellon University. Her research lies at the intersection of machine learning, electric power systems, and climate change mitigation. Specifically, she is interested in creating novel machine learning techniques that incorporate domain knowledge (such as power system physics) to reduce greenhouse gas emissions from the electricity sector. She is a DOE Computational Science Fellow, and also chairs Climate Change AI, an initiative to support work at the intersection of climate change and machine learning.
Borna Poursheikhani is the Chief of Staff at ClimateAi, a Stanford spinout using AI and machine learning to create climate resilience tools for the agriculture sector. In his role, Borna focuses on business development, product discovery, operations, and marketing. He is also the host of Agriculture Adapts, a podcast discussing the challenges and opportunities at the nexus of climate change and agriculture. Prior to joining ClimateAi, Borna worked in the clean energy startup/venture capital space. Borna graduated from UC Berkeley with a master's degree in Civil Engineering: Energy, Civil Infrastructure, and Climate. He was a CELI Fellow in 2018.
The webinar will be held from 3:00-4:00pm Eastern.