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…

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…

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…

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…

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…

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…

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…

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…