# Fall 2018 Colloquia

# Upcoming Events › STOR Colloquium

## August 2018

### STOR Colloquium: Richard Smith, UNC-CH

The Department of Statistics and Operations Research The University of North Carolina at Chapel Hill STOR Colloquium Monday, August 27th, 2018 120 Hanes Hall 3:30pm Richard Smith University of North Carolina-Chapel Hill The First Anniversary of Hurricane Harvey This weekend marks exactly one year since Hurricane Harvey devastated much of the Caribbean and then dumped record levels of rainfall on the city of Houston and its environs. This event also stimulated much scientific work focused on two…

Find out more »## September 2018

### STOR Colloquium: Tingting Zhang, University of Virginia

Tingting Zhang University of Virginia A Bayesian Stochastic-Blockmodel-based Approach for Mapping Epileptic Brain Networks The human brain is a dynamic system consisting of many consistently interacting regions. The brain regions and the influences exerted by each region over another, called directional connectivity, form a directional network. We study normal and abnormal directional brain networks of epileptic patients using their intracranial EEG (iEEG) data, which are multivariate time series recordings of many small brain regions. We propose a high-dimensional state-space…

Find out more »## October 2018

### STOR Colloquium: Vinayak Deshpande, UNC Kenan Flagler

Vinayak Deshpande Kenan Flagler Business School, University of North Carolina at Chapel Hill Data Driven Research: Understanding and Improving Airline Flight Schedules using BTS data The last decade has seen an explosion of operational data that is now available to researchers. In this talk, I will share my experience in conducting research with large datasets made publicly available by the Bureau of Transportation Statistics (BTS). These data sets include flight schedule data, FAA operations and performance data, DOT’s…

Find out more »### STOR Colloquium: Rong Ge, Duke University

Rong Ge Duke University Optimization Landscape for Matrix Completion Matrix completion is a popular approach for recommendation systems. In theory, it can be solved using complicated convex relaxations, while in practice even simple algorithms such as stochastic gradient descent can always converge to the optimal solution. In this talk we will see some new results on the optimization landscape for the natural non-convex objective of matrix completion. In particular, we will show that although the natural objective is non-convex…

Find out more »### STOR Colloquium: Cynthia Rudin, Duke

Cynthia Rudin Duke University New Algorithms for Interpretable Machine Learning in High Stakes Decisions With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed models for medical imaging, and poor bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable models, they come with their own explanations, which are faithful to what the…

Find out more »### STOR Colloquium: Robert Lund, Clemson University

STOR Colloquium Robert Lund Department of Mathematical Sciences Clemson University Multiple Breakpoint Detection: Mixing Documented and Undocumented Changepoints |This talk presents methods to estimate the number of changepoint time(s) and their locations in time-ordered data sequences when prior information is known about some of the changepoint times. A Bayesian version of a penalized likelihood objective function is developed from minimum description length (MDL) information theory principles. Optimizing the objective function yields estimates of the changepoint number(s) and location time(s). Our…

Find out more »## November 2018

### STOR Colloquium: Sven Leyffer, Argonne National Laboratory

Mixed-Integer PDE-Constrained Optimization Many complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. This new class of problems, called mixed-integer PDE-constrained optimization (MIPDECO), must overcome the combinatorial challenge of integer decision variables combined with the numerical and computational complexity of PDE-constrained optimization. Examples of MIPDECOs include the remediation of contaminated sites and the maximization of oil recovery; the design of next-generation solar cells; the layout design of wind-farms; the design and control of gas networks; disaster recovery; and topology…

Find out more »### STOR Colloquium: Quefeng Li, UNC-Chapel Hill

Quefeng Li Department of Biostatistics UNC-Chapel Hill Integrative linear discriminant analysis with guaranteed error rate improvement Numerous empirical studies have found that integrative analysis of multimodal data can result in better statistical performance. However, little theory is known on when and why including more variables in a statistical model can improve the prediction. In the context of two-class classification, we provide a theoretical guarantee that running an integrative linear discriminant analysis on multimodal data achieves smaller misclassification…

Find out more »### STOR Colloquium: Xi Chen, NYU

Xi Chen New York University Statistical Inference for Model Parameters with Stochastic Gradient Descent In this talk, we investigate the problem of statistical inference of the true model parameters based on stochastic gradient descent (SGD) with Ruppert-Polyak averaging. To this end, we propose a consistent estimator of the asymptotic covariance of the average iterate from SGD --- batch-means estimator, which only uses the iterates from SGD. As the SGD process forms a time-inhomogeneous Markov chain, our batch-means estimator…

Find out more »### STOR Colloquium: Tailen Hsing, UMich

Tailen Hsing Department of Statistics University of Michigan Modeling and inference of local stationarity Stationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation to the true state of dependence if we focus on spatial data locally. In this talk, we first review various known approaches for modeling nonstationary spatial data. We then examine the notion of local stationarity in more detail. To illustrate, we focus on the multi-fractional Brownian…

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