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Inferential Artificial Intelligence (iAI)

Seth Flaxman, Associate Professor in the Department of Computer Science at University of Oxford will be presenting “Inferential Artificial Intelligence (iAI): Case Studies in Computational Statistics, Machine Learning, 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. Dr. Flaxman will present his perspective on the emerging field of “inferential Artificial Intelligence” (iAI) through a series of case studies on important global health challenges. He conceives of iAI 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.

Dr. Flaxman will discuss iAI in the context of my work during the COVID-19 pandemic as part of the Imperial College COVID-19 Response Team and the collaborations I am now leading through the Machine Learning & Global Health Network (www.MLGH.net).

Dr. Flaxman Biography:

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) and has worked for the World Health Organization. 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 on Children Affected by COVID-19 and 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.

Event Contact Information:
Robbie Parks


Oct 27 2023


11:00 am - 12:00 pm

Formats (virtual, in person, hybrid)



Allan Rosenfield Building
722 W. 168 St., New York, NY 10032


Columbia University
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