# Spring 2019 Colloquia

# Upcoming Events › STOR Colloquium

## March 2019

### STOR Colloquium: Tucker McElroy, US Census Bureau

Tucker McElroy U.S. Census Bureau Casting Vector Time Series: Forecasting, Imputation, and Signal Extraction In the Context of Big Data Recursive algorithms, based upon the nested structure of Toeplitz covariance matrices arising from stationary processes, are presented for the efficient computation of multi-step ahead forecast error covariances for nonstationary vector time series. Further, we discuss time reversal to forecast the past, and algorithms for imputation of missing values. These quantities are required to quantify multi-step ahead forecast error and…

Find out more »### STOR Colloquium: Jonathan M. Lees, UNC-Chapel Hill

Jonathan M. Lees University of North Carolina at Chapel Hill Geophysical Time Series Analysis on Volcanoes: Can we quantify non-linearity? Most geophysical processes are aperiodic noisy, intermittent and transient. This requires specialized methods for time series analysis, that seek patterns in time series that vary in space and time. I present here examples from research on exploding volcanoes that exhibit tremor that appears to be resonant but likely results from nonlinear feedback systems. The physical models for these observations…

Find out more »### STOR Colloquium: Yuan Liao, Rutgers University

Yuan Liao Rutgers University Factor-Driven Two-Regime Regression using Mixed Integer Programming We propose a two-regime regression model where the switching between the regimes is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a much larger data set. We show that the optimization problem can be reformulated as mixed integer optimization and present two alternative computational algorithms: (1) MI quadratic programming and (2) MI…

Find out more »## April 2019

### STOR Colloquium: Serhan Ziya, UNC-Chapel Hill

Serhan Ziya University of North Carolina at Chapel Hill Service operations with a focus in healthcare: a partial and subjective overview of related research in STOR This talk will provide an overview of some of the research projects in which the speaker is either currently an active participant or hopes to help initiate in the near future. The main goal is to create awareness and generate interest in the area of service operations particularly as it relates to…

Find out more »## September 2019

### Colloquium: Peter J. Mucha, UNC-Chapel Hill

Peter J. Mucha The University of North Carolina at Chapel Hill Department of Mathematics Communities in Multilayer Networks Community detection describes the organization of a network in terms of patterns of connection, identifying tightly connected structures known as communities. A wide variety of methods for community detection have been proposed, with a number of software packages available for performing community detection. In the past decade, there has been increased interest in multilayer networks, a general framework that can be used…

Find out more »## October 2019

### STOR Colloquium: Tong Wang, University of Iowa

Dr. Tong Wang Tippie College of Business University of Iowa Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Interpretable machine learning has received increasing interest in recent years, especially in domains where humans are involved in the decision-making process. However, the possible loss of the task performance for gaining interpretability is often inevitable, especially for large datasets or complicated tasks. This performance downgrade puts practitioners in a dilemma of choosing between a top-performing black-box…

Find out more »### STOR Colloquium: Heping Zhang, Yale

Back to the Basics: Residuals and Diagnostics for Generalized Linear Models

Find out more »### STOR Colloquium: Shujie Ma, UC Riverside

Shujie Ma University of California, Riverside How many communities are there in a network? Advances in modern technology have facilitated the collection of network data which emerge in many fields including biology, bioinformatics, physics, economics, sociology and so forth. Network data often have natural communities which are groups of interacting objects (i.e., nodes); pairs of nodes in the same group tend to interact more than pairs belonging to different groups. Community detection then becomes a very important task,…

Find out more »## November 2019

### STOR Colloquium: Jianqing Fan, Princeton

Jianqing Fan Princeton University Statistical Inference on Membership Profiles in Large Networks Network data is prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. The nodes can be broadly defined such as individuals, economic entities, documents, or medical disorders in social, economic, text, or health networks. Yet a simple question of how to precisely quantify the statistical uncertainty associated with the identification of latent…

Find out more »## January 2020

### STOR Colloquium: Anna Little, Michigan State University

Robust Statistical Procedures for Noisy, High-dimensional Data This talk addresses two topics related to robust statistical procedures for analyzing noisy, high-dimensional data: (I) path-based spectral clustering and (II) robust multi-reference alignment. Both methods must overcome a large ambient dimension and lots of noise to extract the relevant low dimensional data structure in a computationally efficient way. In (I), the goal is to partition the data into meaningful groups, and this is achieved by a novel approach which combines a…

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