The key thrust behind the fast emerging area of AI for Social Impact (AISI) has been to apply AI research to address societal challenges. AI has great potential to provide tremendous societal benefits, having been successfully deployed in areas spanning public health, environmental sustainability, education, public welfare, among many others. In AI, we have just recently begun to define this topic as its own area of research, and we have just started understanding that AISI includes more than simply providing methodological advances in terms of newer models and algorithms. During this moderated discussion, we will hear from four early-career leaders in the field of AI for Social Impact on where AISI is going, the direction where it should go, and how we can get there.
Fei Fang is Leonardo Assistant Professor at the Institute for Software Research in the School of Computer Science at Carnegie Mellon University. Before joining CMU, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University, hosted by David Parkes and Barbara Grosz. She received her Ph.D. from the Department of Computer Science at the University of Southern California advised by Milind Tambe (now at Harvard).
Her research lies in the field of artificial intelligence and multi-agent systems, focusing on integrating machine learning with game theory. Her work has been motivated by and applied to security, sustainability, and mobility domains, contributing to the theme of AI for Social Good. She is the recipient of the IJCAI-21 Computers and Thought Award. She was named to IEEE Intelligent Systems’ “AI’s 10 to Watch” list for 2020. Her work has won the Best Paper Runner-Up at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI’21), Distinguished Paper at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI’18), Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16), the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). She received an NSF CAREER Award in 2021. Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award, and is selected to be the winner of the William F. Ballhaus, Jr. Prize for Excellence in Graduate Engineering Research as well as the Best Dissertation Award in Computer Science at the University of Southern California. Her work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work has led to the deployment of PAWS (Protection Assistant for Wildlife Security) in multiple conservation areas around the world, which provides predictive and prescriptive analysis for anti-poaching effort.
Lynn Kaack is Assistant Professor of Computer Science and Public Policy at the Hertie School. Her work focuses on methods from statistics and machine learning to inform climate mitigation policy across the energy sector, and on climate-related AI policy. She is also a co-founder and chair of the organization Climate Change AI. Previously she was Postdoctoral Researcher and Lecturer in the Energy Politics Group at ETH Zürich. She obtained a PhD in Engineering and Public Policy and a Master's in Machine Learning from Carnegie Mellon University, as well as a MS and BS in Physics from the Free University of Berlin.
Dr. Danaë Metaxa (they/them) is an incoming Assistant Professor of Computer and Information Science at the University of Pennsylvania, with a secondary appointment in Penn’s Annenberg School for Communication. Dr. Metaxa received their PhD in Computer Science from Stanford University in 2021, and is currently a Postdoctoral Researcher at Stanford’s Center for Philanthropy and Civil Society, working on the Program for Democracy and the Internet. Previously, Danaë received dual undergraduate BA degrees from Brown University in Computer Science and Science & Society. During their academic career they have had the honor of being recognized by several awards including an NSF Graduate Fellowship and an Anita Borg Memorial Scholarship from Google. Their research has been published and awarded at top computer science conference venues including CHI and CSCW.
Bryan Wilder is Schmidt Science Fellow at Carnegie Mellon University and Harvard School of Public Health. In Fall 2022, he will join CMU as an Assistant Professor in the Machine Learning Department. His research focuses on the intersection of optimization, machine learning, and social networks, motivated by applications to public health. He was previously supported by the NSF and Siebel Fellowships and his dissertation was recognized with the 2021 IFAAMAS Victor Lesser Distinguished Dissertation Award. His work has been a finalist for best paper awards at ICML, AAMAS, and the INFORMS Doing Good with Good OR competition.