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Past Events › Graduate Seminar

October 2019

Grad Student Seminar: Miheer Dewaskar

October 25, 2019 @ 3:30 pm - 4:30 pm

Miheer Dewaskar UNC-Chapel Hill   Asymptotic analysis of the power of choice phenomenon Suppose that n balls are to be sequentially placed into n bins with the objective of keeping the maximum load of the bins small. In absence of a central dispatcher, and in order to minimize the communication overhead, each incoming ball chooses d bins uniformly at random and goes into the bin with the smallest load among its d choices. The maximum bin load for d =…

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November 2019

Grad Student Seminar: Carson Mosso, Jonghwan Yoo

November 22, 2019 @ 3:30 pm - 4:30 pm

Carson Mosso - Latent Association Mining in Binary Data   We will introduce a new data mining method for binary valued data, called Latent Association Mining in Binary Data. The origin of this problem is in market basket analysis, where binary valued data is common, and typically falls under the branch of data mining called Association Rule Mining. However, the problem can be generalized by mining for correlation between variables in various types of datasets, e.g., text or gene expression…

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January 2020

Graduate Seminar: Mark He

January 17, 2020 @ 3:30 pm - 4:30 pm

Mark He UNC-Chapel Hill   Intertemporal Community Detection in Human Mobility Networks: Case Studies of Bikeshare Systems in US Cities   We introduce a community detection method that finds clusters in network time-series by introducing a method that finds significantly interconnected nodes across time that are either increasing, decreasing, or constant in connectivity. Significance of nodal connectivity within a set are judged as by the Weighted Configuration Null Model at each time-point, then a novel significance-testing scheme is used to…

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March 2020

Graduate Seminar: Alex Touzov

March 6, 2020 @ 3:30 pm - 4:30 pm

Graduate Seminar Friday, March 6th, 2020 120 Hanes Hall 3:30pm   Alex Touzov UNC-Chapel Hill   Weak infeasibility in semidefinite programming: a complete characterization and generating all instances   Abstract: In this work, we analyze weakly infeasible semidefinite programs (SDPs). These SDPs are infeasible, but an arbitrarily small perturbation can make them feasible.   Weakly infeasible SDPs appear in many guises, some classical and some more recent: 1) as asymptotes of the semidefinite cone; as 2) difficult SDPs, which are…

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September 2020

Graduate Seminar: Kevin O’Connor

September 11, 2020 @ 3:30 pm - 4:30 pm

Optimal Transport for Stationary Markov Chains via Policy Iteration In this talk, we discuss an extension of optimal transport techniques to stationary Markov chains from a computational perspective. In this context, we show that the standard optimal transport problem does not capture differences in the dynamics of the two chains. Instead, we study a new problem, called the optimal transition coupling problem, in which the optimal transport problem is constrained to the set of stationary Markovian couplings satisfying a certain…

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Graduate Student Seminar

September 18, 2020 @ 3:30 pm - 4:30 pm

Dr. Shankar Bhamidi, Director of Graduate Studies, will host a zoom chat with grad students.  Come with questions, or just check in and enjoy social time.

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October 2020

Graduate Seminar: Quoc Tran-Dinh

October 2, 2020 @ 3:30 pm - 4:30 pm

Quoc Tran-Dinh Statistics & Operations Research Dept., UNC-CH Efficient Stochastic Gradient-Based Algorithms with Biased Variance-Reduced Estimators In this talk, we discuss some recent progress in stochastic gradient-based methods using biased variance-reduced estimators to approximate a stationary point or a KKT point of non-convex problems such as stochastic non-convex optimization, stochastic compositional optimization, and stochastic minimax problems. More specifically, we introduce a new class of hybrid biased variance-reduced estimators that combines the well-known SARAH (Nguyen et al (2017)) and the classical…

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Grad Student Seminar: Adam Waterbury

October 30, 2020 @ 3:30 pm - 4:30 pm

Stochastic Approximation of Quasi-Stationary Distributions We propose two numerical schemes for approximating quasi-stationary distributions (QSD) of finite state Markov chains with absorbing states. Both schemes are described in terms of certain interacting chains in which the interaction is given in terms of the total time occupation measure of all particles in the system. The schemes can be viewed as combining the key features of the two basic simulation-based methods for approximating QSD originating from the works of Fleming and Viot…

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November 2020

Grad Student Seminar: Michael Conroy

November 6, 2020 @ 3:30 pm - 4:30 pm

Michael Conroy UNC-Chapel Hill Efficient rare-event simulation for branching processes In this talk I’ll discuss some of my past, current, and future work with importance sampling schemes for maxima of branching processes. In a recent paper, my collaborators and I developed a strongly efficient and unbiased estimator for tail events of the maximum of a branching random walk with perturbation (or a Galton-Watson process on a random tree). The sampling procedure relies on a change of measure applied to the…

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Grad Student Seminar: Samopriya Basu, Jack Prothero

November 20, 2020 @ 3:30 pm - 4:30 pm

Samopriya Basu Fiducial inference for SDEs with constant diffusion In this talk, I will talk about my research with my advisor Prof. Jan Hannig on carrying out fiducial inference for stochastic ordinary differential equations with constant diffusion coëfficient. The diffusion coëfficient σ is unknown and the drift term can depend on any number of unknown parameters β, and the task is to come up with a data-dependent distribution estimator Fid(·) on the parameter space Θ ⊂ ℝ+ × ℝp+1, called…

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