[Sds-seminars] S&DS Seminar, Seth Flaxman, 03/04/24, 4pm, KT, "Inferential Machine Learning: Statistics, Data Science, and Public Policy"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  


SETH FLAXMAN, University of Oxford


Date: Monday, March 04, 2024

Time: 4:00PM to 5:00PM

Kline Tower
<http://maps.google.com/?q=219+Prospect+Street%2C+13+Floor%2C+Rm+1327%2C+New
+Haven%2C+CT%2C+06511%2C+us> see map 

Location: 219 Prospect Street, 13 Floor, Rm 1327

New Haven, CT 06511

Zoom Link: https://yale.zoom.us/j/94223816617 Meeting ID: 942 2381 6617

 <http://www.sethrf.com/> Website

 

Inferential Machine Learning: Statistics, Data Science, and Public Policy

 

Information and Abstract: 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 Machine
Learning" (iML) 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. I will discuss iML 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 ( <http://www.mlgh.net/> www.MLGH.net).

 

Speaker: Seth Flaxman, Associate Professor, Department of Computer Science,
University of Oxford ( <http://www.sethrf.com/> www.sethrf.com)

 

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) and has worked for the World Health Organization in Geneva.
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 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 ( <http://www.mlgh.net/> www.MLGH.net) of researchers spanning three
continents with a kickoff workshop held in Kigali, Rwanda at ICLR in 2023.

 

3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13 floor,
there will be light snacks and beverages in the kitchen area.

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/

 

Department of Statistics and Data Science

Yale University

Kline Tower
219 Prospect Street
New Haven, CT 06511

 

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