Applied Risk and Probability Seminar
Thursday,
November 8, 2018
1:10 PM - 2:00 PM
Speaker: Subhabrata Sen (MIT)
Title: Sampling convergence for random graphs: graphexes and multigraphexes
Abstract: We will look at structural properties of large, sparse random graphs through the lens of sampling convergence (Borgs, Chayes, Cohn and Veitch ’17). Sampling convergence generalizes left convergence to sparse graphs, and describes the limit in terms of a graphex. We will introduce this framework and motivate the components of a graphex. Subsequently, we will discuss the graphex limit for several sparse random (multi)graphs of practical interest.
This is based on joint work with Christian Borgs, Jennifer Chayes, and Souvik Dhara.
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 Statis- tics, 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”. Sub- habrata’s research interests include random combinatorial optimization, random graphs, spin glasses, and hypothesis testing.
Title: Sampling convergence for random graphs: graphexes and multigraphexes
Abstract: We will look at structural properties of large, sparse random graphs through the lens of sampling convergence (Borgs, Chayes, Cohn and Veitch ’17). Sampling convergence generalizes left convergence to sparse graphs, and describes the limit in terms of a graphex. We will introduce this framework and motivate the components of a graphex. Subsequently, we will discuss the graphex limit for several sparse random (multi)graphs of practical interest.
This is based on joint work with Christian Borgs, Jennifer Chayes, and Souvik Dhara.
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 Statis- tics, 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”. Sub- habrata’s research interests include random combinatorial optimization, random graphs, spin glasses, and hypothesis testing.
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