Jean-Gabriel Young
Assistant Professor of Mathematics and Statistics, University of Vermont
Talk recording
Machine learning systems now routinely use embeddings in thousands of dimensions to extract patterns from large-scale network data. Should we embrace this data revolution and let go of the simpler theories of yore—the likes of the S1 and Bradley-Terry models? In this talk, I will argue that low-dimensional embedding can find concise, interpretable patterns in networks and thus have a place in any modern data science stack. I will illustrate this point through a number of stories about social hierarchies and decision-making.
About the speaker
Jean-Gabriel Young is an Assistant Professor of Mathematics and Statistics at The University of Vermont, where he also holds faculty affiliations at the Translational Global Infectious Diseases Research Center and the Vermont Complex Systems Center. Professor Young’s research is at the intersection of statistical inference, epidemiology, and complex systems. Previously, he was a James S. McDonnell Foundation Fellow at the Center for the Study of Complex Systems of the University of Michigan, mentored by Professor Mark Newman. He obtained his PhD in Physics from Université Laval, under the guidance of Prof. Louis J. Dubé and Prof. Patrick Desrosiers.
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