# 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. Vladas Pipiras.

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:

## Past Events › STOR Colloquium

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## STOR Colloquium: Mariana Olvera-Cravioto, University of California-Berkeley

Directed complex networks and ranking algorithms In the first part of this talk I will discuss a family of inhomogeneous directed random graphs for modeling complex networks such as the web graph, Twitter, ResearchGate, and other social networks. This class of graphs includes as a special case the classical Erdos-Renyi model, and can be used to replicate almost any type of predetermined degree distributions, in particular, power-law degrees such as those observed in most real-world networks. I will mention during…

Find out more »## STOR Colloquium: Zhiyi Zhang, UNC Charlotte

Zhiyi Zhang University of North Carolina at Charlotte Title: Statistical Implications of Turing’s Formula Abstract: This talk is organized into three parts. Turing’s formula is introduced. Given an iid sample from a countable alphabet under a probability distribution, Turing’s formula (introduced by Good (1953), hence also known as the Good-Turing formula) is a mind-bending non-parametric estimator of total probability associated with letters of the alphabet that are NOT represented in the sample. Many of its statistical properties were…

Find out more »## STOR Colloquium: Hongtu Zhu, MD Anderson

Hongtu Zhu University of North Carolina at Chapel Hill, and The University of Texas MD Anderson Cancer Center Title: Statistical Challenges, Opportunities, and Strategies in Large-Scale Medical Studies With the rapid growth of modern technology, many biomedical studies have collected data across different sources (e.g., imaging, genetics, and clinical) in an unprecedented scale. The integration of such ultra high-dimensional data raises many statistical challenges, rendering most existing statistical methods and old data platform no longer suitable and thus underscoring…

Find out more »## CANCELLED STOR Colloquium

Philip Ernst Rice University Title: Yule's "Nonsense Correlation" Solved! Abstract: In this talk, I will discuss how I recently resolved a longstanding open statistical problem. The problem, formulated by the British statistician Udny Yule in 1926, is to mathematically prove Yule's 1926 empirical finding of ``nonsense correlation.” We solve the problem by analytically determining the second moment of the empirical correlation coefficient of two independent Wiener processes. Using tools from Fredholm integral equation theory, we calculate the second…

Find out more »## STOR Colloquium: Ilse Ipsen, North Carolina State University

Ilse Ipsen North Carolina State University Randomized Algorithms for Matrix Computations The emergence of massive data sets, over the past fifteen or so years, has led to the development of Randomized Numerical Linear Algebra. Fast and accurate randomized matrix algorithms are being designed for applications like machine learning, population genomics, astronomy, nuclear engineering, and optimal experimental design. We give a flavour of randomized algorithms for the solution of least squares/regression problems and, if time permits, for the computation of…

Find out more »## STOR Colloquium: Jong-Shi Pang, U. of Southern California

Jong-Shi Pang University of Southern California Title: Structural Properties of Affine Sparsity Constraints Abstract: We introduce a new constraint system for sparse variable selection in statistical learning. Such a system arises when there are logical conditions on the sparsity of certain unknown model parameters that need to be incorporated into their selection process. Formally, extending a cardinality constraint, an affine sparsity constraint (ASC) is defined by a linear inequality with two sets of variables: one set of continuous variables…

Find out more »## STOR Colloquium: Ion Necoara, Polytechnic University of Bucharest

Ion Necoara Polytechnic University of Bucharest Conditions for linear convergence of (stochastic) first order methods For convex optimization problems deterministic first order methods have linear convergence when the objective function is smooth and strongly convex. Moreover, under the same conditions - smoothness and strong convexity - sublinear convergence rates have been derived for stochastic first order methods. However, in many applications (machine learning, statistics, control, signal processing) the strong convexity condition does not hold, but the objective function…

Find out more »## STOR Colloquium: Hao Chen, UC Davis

Change-point detection for locally dependent data Local dependence is common in multivariate and non-Euclidean data sequences, such as network data. We consider the testing and estimation of change-points in such sequences. A new way of permutation, circular block permutation with a randomized starting point, is proposed and studied for a scan statistic utilizing graphs representing the similarity between observations. The proposed permutation approach could correctly address for local dependence and make it possible the theoretical treatments for the non-parametric…

Find out more »## STOR Colloquium: Iain Carmichael, UNC-Chapel Hill

Title: Data science and the undergraduate curriculum Last semester we developed and taught a new course titled “Introduction to Data Science” for the undergraduate analytics major at UNC (see https://idc9.github.io/stor390/). The core topics of the class were: programming in R (working with data, visualization, functions/loops/conditionals), data analysis (exploratory, predictive and inferential), acquiring data (APIs, web scraping), communication (e.g. literate programming, effective visualization), and some additional topics (text data and data ethics/inequality). This course differed from existing courses in a…

Find out more »## STOR Colloquium: Philip Ernst, Rice

Yule's "Nonsense Correlation" Solved! In this talk, I will discuss how I recently resolved a longstanding open statistical problem. The problem, formulated by the British statistician Udny Yule in 1926, is to mathematically prove Yule's 1926 empirical finding of ``nonsense correlation.” We solve the problem by analytically determining the second moment of the empirical correlation coefficient of two independent Wiener processes. Using tools from Fredholm integral equation theory, we calculate the second moment of the empirical correlation to obtain a…

Find out more »