Associate Prof. Dr. Seth Flaxman

"Inferential Artificial Intelligence (iAI) for Public Policy and Global Health"

Machine learning is the computational beating heart of the modern AI renaissance. Behind the hype, a range of machine learning and computational statistical methods are quietly revolutionizing our approach to difficult statistical and scientific inference problems. I will present my perspective on the emerging field of “inferential Artificial Intelligence” (IAI) through a series of case studies on important public policy challenges. I conceive of iML as a big tent, encompassing modern probabilistic programming, replicable data scientific workflows, methods for assessing Big Data quality, uncertainty quantification, active learning, and a range of computational and deep learning approaches to transform applied statistical analyses.

Bio: Seth Flaxman is an associate professor in the Department of Computer Science at Oxford. Originally from the Chicago area, he received his PhD in 2015 from Carnegie Mellon University in machine learning and public policy (School of Computer Science and Heinz College of Information Systems and Public Policy). Seth’s research is on spatiotemporal statistics and Bayesian machine learning, applied to public policy, global health and social science. He was part of the Imperial College COVID-19 Response Team, leading a number of publications on non-pharmaceutical interventions, computational epidemiology, and COVID-19 orphanhood. He has published on filter bubbles / echo chambers in media, the Big Data paradox, and the regulation of machine learning algorithms. He is the statistical lead for the Global Reference Group for Children Affected by Crisis. Seth won the Samsung AI Researcher of the Year Award (2020) and the SPI-M-O Award for Modelling and Data Support (2022) for modeling advice provided to the UK government during the COVID-19 pandemic. In 2022, he co-founded the Machine Learning & Global Health network (www.MLGH.net) of researchers spanning three continents with a kickoff workshop held in Kigali, Rwanda at ICLR in 2023.

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