# Graduate Seminar

A seminar organized by/for the graduate students.

# Past Events › Graduate Seminar

## February 2019

### Graduate Student Seminar: Haipeng Gao

Bayesian Inference for Stochastic Cusp Catastrophe Model (Under direction of Prof. Chuanshu Ji) Continuous-time diffusion processes have been widely used in modern financial economics to model the stochastic behavior of economic variables such as interest rates, exchange rates, and stock prices. The Black Scholes model, the Vasicek model, and the Cox-Ingersoll-Ross model, all assume that the underlying state variables follow diffusion processes. If one believes that the observed values are generated according to some parametric specification, developing rigorous statistical…

Find out more »### STOR Colloquium: Elina Robeva, MIT

Elina Robeva Massachusetts Institute of Technology Maximum likelihood estimation under total positivity Nonparametric density estimation is a challenging statistical problem -- in general the maximum likelihood estimate (MLE) does not even exist! Introducing shape constraints allows a path forward. In this talk I will discuss non-parametric density estimation under total positivity (i.e. log-supermodularity). Though they possess very special structure, totally positive random variables are quite common in real world data and exhibit appealing mathematical properties. Given i.i.d. samples from…

Find out more »## May 2019

### Grad Student Seminar: Yunxiao Liu, Uber

Spatio-Temporal Forecasting at Uber In this talk I am going to give a high-level introduction to the data science projects we have been working on at Marketplace Forecasting at Uber. Marketplace Forecasting at Uber stands at the upstream of the marketplace optimization flow and generates time series and machine learning models for quantities like supply, demand and marketplace balance for Uber. These forecasts, at varying levels of spatial/temporal granularity and with different forecast horizons, provide a forward view of marketplace…

Find out more »## September 2019

### Grad Student Seminar: Michael Conroy & Adam Waterbury

Michael Conroy- UNC-Chapel Hill A direct Approach to Renewal Theory on Trees In a variety of applications ranging from computer science to statistical physics, a class of recursive stochastic fixed-point equations appear. Such recursions admit so-called special endogenous solutions constructed on a random weighted tree formed from i.i.d. copies of the inputs to the recursion. We are interested in the tail behavior of the endogenous solution a max-type recursion that arises in the analysis of the branching random walk and…

Find out more »### Grad Student Seminar: Jack Prothero & Marie Dueker

Jack Prothero - UNC-Chapel Hill Extracting Signal and Noise from Large Matrices Discerning a low-rank signal from a large, noisy data matrix is a classic problem in statistics and signal processing. We review a fundamental result in random matrix theory and how it applies to recent results on optimal singular value thresholding and shrinkage for signal extraction. In toy examples we find that these thresholding procedures often fail to capture as much signal as is theoretically possible. Our main contribution…

Find out more »## October 2019

### Grad Student Seminar: Michael Conroy

Asymptotic optimality in resource sharing networks

Find out more »### Grad Student Seminar: Miheer Dewaskar

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 =…

Find out more »## November 2019

### Grad Student Seminar: Carson Mosso, Jonghwan Yoo

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…

Find out more »## January 2020

### Graduate Seminar: Mark He

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…

Find out more »## 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…

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