What can we learn from low-dimensions representations of networks? Jean-Gabriel Young

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 simpler network theories---S1 models, Bradley-Terry models, and so on? In this talk, I will argue that low-dimensional embedding can reveal powerful yet interpretable network patterns 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. Jean-Gabriel Young is an Assistant Professor of Mathematics and Statistics at The University of Vermont, VT, USA, where he co-directs the joint lab. His research focuses on 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 at the University of Michigan, mentored by Prof. Mark Newman. He obtained his PhD in Physics from Université Laval, under the guidance of Prof. Louis J. Dubé and Prof. Patrick Desrosiers. This seminar is part of the Network Seminar series at LPI Paris (https://interactiondatalab.com/networ....