# Graduate Seminar

A seminar organized by/for the graduate students.

# Past Events › Graduate Seminar

## March 2020

### Graduate Seminar: Alex Touzov

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…

Find out more »## September 2020

### Graduate Seminar: Kevin O’Connor

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…

Find out more »### Graduate Student Seminar

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.

Find out more »## October 2020

### Graduate Seminar: Quoc Tran-Dinh

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…

Find out more »### Grad Student Seminar: Adam Waterbury

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…

Find out more »## November 2020

### Grad Student Seminar: Michael Conroy

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…

Find out more »### Grad Student Seminar: Samopriya Basu, Jack Prothero

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…

Find out more »## February 2021

### Grad Student Seminar: Stefanos Kechagias, SAS

Stefanos Kechagias Analytical Consulting & Enterprise Solutions, SAS Institute Scratch out “learn TensorFlow” from your New Year bucket list. In goes “learn how to tell valuable stories” In this talk we will discuss three Story Telling Mediums statisticians employ to share their expert knowledge: scientific writing, technical presentations and development of statistical software. First, we will lay out proper foundations of scientific writing by placing the reader at the center of a statistician’s thought process. Under this reader-centric prism we…

Find out more »## March 2021

### Graduate Seminar: Alexander Murph

Generalized Fiducial Inference on Differentiable Manifolds I’ll discuss the problem of defining a general fiducial density on an implicitly defined differentiable manifold and introduce our recent solution. Our proposed density extends the usual generalized fiducial distribution (GFD) by projecting the Jacobian differential onto the space that only allows directions of change that satisfy some constraint function. This calculation is shown to be simple to compute and exists under minor smoothness assumptions. To circumvent the need for an intractable marginal integral…

Find out more »### Graduate Seminar: Taylor Petty

Forensic DNA Mixture Deconvolution with Next-Generation Sequencing Next-generation sequencing (NGS) provides a higher-resolution view of DNA mixtures processed from crime scenes than the current practice of Capillary Electrophoresis (CE). Whereas CE merely counts Short Tandem Repeats (STRs) and ignores flanking regions, NGS considers allelic variation and mutation within STRs as well as flanking regions. New methods are required in order to take advantage of this new, richer data type, and precision is of the essence since both false positives and…

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