# Colloquia

Unless otherwise noted, all talks are in 120 Hanes Hall, at 3:30 PM on Mondays. Prior to the talk, from 3:00-3:30 PM, the audience is invited for refreshments in the lounge on the 3rd floor of Hanes Hall. If you would like to suggest a speaker, or get on our mailing list, please send an email to Dr. Gabor Pataki or Dr. Kai Zhang.

In addition to weekly colloquia and seminars, Hotelling lectures are held to honor the memory of Professor Harold Hotelling, first chairman of the “Department of Mathematical Statistics.”

Quick access to previous talks:

- Spring 2019 Colloquia
- Fall 2018 Colloquia
- Spring 2018 Colloquia
- Fall 2017 Colloquia
- Spring 2017 Colloquia
- Fall 2016 Colloquia
- Spring 2016 Colloquia
- Talks given before 2016

# Past Events › STOR Colloquium

## January 2020

### STOR Colloquium: Eric Lock, University of Minnesota

Eric Lock University of Minnesota School of Public Health Bidimensional Linked Matrix Decomposition for Pan-Omics Pan-Cancer Analysis Several recent methods address the integrative dimension reduction and decomposition of linked high‐content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). This is limiting for data that take…

Find out more »## September 2020

### STOR Colloquium: Themis Sapsis, MIT

Output-Weighted Active Sampling for Bayesian Uncertainty Quantification and Prediction of Rare Events Themis Sapsis We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian uncertainty quantification of rare events. The approach follows the paradigm of active learning, whereby existing samples of a black-box function are utilized to optimize the next most informative sample. The proposed method aims to take advantage of the fact that some input directions of the black-box function…

Find out more »## October 2020

### STOR Colloquium: Patrick Combettes, NCSU

Patrick Louis Combettes North Carolina State University Perspective Functions and Applications In this talk I will discuss mathematical and computational issues pertaining to perspective functions, a powerful concept that permits to extend a convex function to a jointly convex one in terms of an additional scale variable. Applications in inverse problems and statistics will be presented.

Find out more »### STOR Colloquium: Lihua Lei, Stanford

Lihua Lei Stanford University Hierarchical Community Detection for Heterogeneous and Multi-scaled Networks Real-world networks are often hierarchical, heterogeneous, and multi-scaled, while the idealized stochastic block models that are extensively studied in the literature tend to be over-simplified. In a line of work, we propose several top-down recursive partitioning algorithms which start with the entire network and divide the nodes into two communities by certain spectral clustering methods repeatedly, until a stopping rule indicates no further community structures. For these…

Find out more »## November 2020

### STOR Colloquium: Jacob Bien, USC

Jacob Bien University of Southern California Tree-Based Aggregation of Rare Features for Prediction It is common in modern prediction problems for many features to be counts of rarely occurring events. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from biology (e.g., rare species within a microbiome) to natural language processing (e.g., rare words within an online hotel review). We show, both theoretically and empirically, that not explicitly accounting for…

Find out more »### STOR Colloquium: Mayya Zhilova, Georgia Tech

Mayya Zhilova Georgia Institute of Technology Nonasymptotic Edgeworth-type expansions for growing dimension. In this talk I would like to discuss the problem of establishing higher order accuracy of bootstrapping procedures and (non-)normal approximation in the multivariate or high-dimensional setting. This topic is important for numerous problems in statistical inference and applications concerned with confidence estimation and hypothesis testing, and involving a growing dimension of random data or unknown parameter. In particular, I will focus on higher-order expansions for the…

Find out more »### STOR Colloquium: Kavita Ramanan, Brown University

Kavita Ramanan Brown University Large Deviations of Random Projections of High-dimensional Measures Properties of random projections of high-dimensional probability measures are of interest in a variety of fields, including asymptotic convex geometry, and high-dimensional statistics and data analysis. A particular question of interest is to identify what properties of the high-dimensional measure are captured by its lower-dimensional projections. While fluctuations of these projections have been well studied over the past decade, we describe more recent work on both annealed…

Find out more »## February 2021

### Colloquium: Dmitriy Drusvyatskiy, University of Washington

Dmitriy Drusvyatskiy University of Washington at Seattle Stochastic methods for nonsmooth nonconvex optimization Stochastic iterative methods lie at the core of large-scale optimization and its modern applications to data science. Though such algorithms are routinely and successfully used in practice on highly irregular problems (e.g. deep neural networks), few performance guarantees are available outside of smooth or convex settings. In this talk, I will describe a framework for designing and analyzing stochastic methods on a large class of nonsmooth and…

Find out more »## March 2021

### STOR Colloquium: David Matteson, Cornell

Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is…

Find out more »### STOR Colloquium: Linan Chen, McGill

A Glimpse into Random Geometry: from Brownian motion to Gaussian Free Field For "random curve", a natural and classical model is Brownian motion; when it comes to "random surface", a promising candidate model is Gaussian free field (GFF), which can be seen as the analog of Brownian motion with multi-dimensional time parameters. In this talk, we will introduce GFF from the viewpoint of infinite dimensional Gaussian measure, discuss some problems arising from the study of geometric properties of GFF, and…

Find out more »