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Title: Network data: Modeling and Statistical AnalysisAbstract: Network data arises frequently in modern scientific applications. These networks often exhibit specific characteristics like edge sparsity, heavy-tailed degree distribution etc. Some broad challenges arising in the analysis of such datasets include (i) developing flexible, interpretable models for networks, (ii) provably recovering latent structure from such data, and (iii) testing for goodness of fit. In this talk, we will discuss recent progress in addressing very specific instantiations of these challenges. In particular, we will
Short Bio: Subhabrata Sen is a Schramm Postdoctoral Fellow at the Department of Mathematics, Massachusetts Institute of Technology, and Microsoft Research (New England). He completed his Ph.D. in 2017 from the Department of Statistics, Stanford University, where he was advised jointly by Prof. Amir Dembo and Prof. Andrea Montanari. He was awarded the “Probability Dissertation Award” for his thesis “Optimization, Random Graphs, and Spin Glasses”. Subhabrata’s research interests include random graphs, structured detection, and combinatorial optimization.