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Practical Challenges in Applying ML to Climate Change
Jennifer Chayes · John Platt · Felix Creutzig · Marta Gonzalez · Craig Miller

Sat Dec 14 05:00 PM -- 06:00 PM (PST) @ None

Author Information

Jennifer Chayes (Microsoft Research)

Jennifer Chayes is Technical Fellow and Managing Director of Microsoft Research New England, New York City, and Montreal. She was for many years Professor of Mathematics at UCLA. She is author of over 140 academic papers and inventor of over 30 patents. Her research areas include phase transitions in computer science, structural and dynamical properties of networks, graph theory, graph algorithms, and computational biology. She is one of the inventors of the field of graphons, which are now widely used in the machine learning of massive networks. Chayes’ recent work focuses on machine learning, broadly defined. Chayes holds a BA in physics and biology from Wesleyan, where she graduated first in her class, and a PhD in physics from Princeton. She was a postdoctoral fellow at Harvard and Cornell. She is the recipient of the NSF Postdoc Fellowship, the Sloan Fellowship, the UCLA Distinguished Teaching Award, and the Anita Borg Institute Women of Leadership Vision Award. She has twice been a member of the Institute for Advanced Study in Princeton. Chayes is Fellow of the American Association for the Advancement of Science, the Fields Institute, the Association for Computing Machinery, and the American Mathematical Society, and the American Academy of Arts and Sciences. She is the winner of the 2015 John von Neumann Lecture Award, the highest honor of the Society of Industrial and Applied Mathematics. In 2016, she received an Honorary Doctorate from Leiden University. Chayes serves on numerous scientific boards and committees. She is a past VP of the American Mathematical Society, past Chair of Mathematics for the Association for the Advancement of Science, and past Chair of the Turing Award Selection Committee. She is also committed to diversity in the science and technology, and serves on many boards to increase representation of women and minorities in STEM.

John Platt (Google)
Felix Creutzig (TU Berlin, MCC)
Marta Gonzalez (UC Berkeley)

Marta C. Gonzalez is Associate Professor of City and Regional Planning and Civil and Environmental Engineering at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). With the support of several companies, cities and foundations, her research team develops computer models to analyze digital traces of information mediated by devices. They process this information to manage the demand in urban infrastructures in relation to energy and mobility. Her recent research uses billions of mobile phone records to understand the appearance of traffic jams and the integration of electric vehicles into the grid, smart meter data records to compare the policy of solar energy adoption and card transactions to identify habits in spending behavior. Prior to joining Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT, a member of the Operations Research Center and the Center for Advanced Urbanism. She is a member of the scientific council of technology companies such as Gran Data, PTV and the Pecan Street Project consortium.

Craig Miller (National Rural Electric Cooperative Association)

Craig Miller is the Chief Scientist of the National Rural Electric Cooperative Association and leads research for the 900+ electric cooperative utilities in the United States serving 35 million customers, and managing half of the distribution line in the U.S. The research portfolio is largely focused on advanced analytics for agile grid control. Artificial Intelligence is a substantial element in this work with an increasing role as the grid become more data driven. He holds a Ph.D. in Energy Systems Engineering, and was awarded a gold medal by the Smithsonian Institution for “Heroic Achievement in the Advancement of Information Technology”. His interest in AI can be traced to his undergraduate thesis in 1970 which employed neural net methods in a manner that looks hopelessly archaic.

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