Advanced Research Initative

The Advanced Research Initiative of the Department of Mathematics and College of Science invites distinguished researchers to Marshall to speak with both general and specialized audiences and interact with graduate and undergraduate students.

Spring 2014

Photo of Dr. Hirst Dr. Jeffry L. Hirst
Professor of Mathematics
Appalachian State University
“Alan and Ada's Theoretical Machines”
Monday, March 10, 2014 • Shawkey Room, MSC • 4:00pm • Flyer
Ada Lovelace is often called “the first computer programmer,” though she died a century before the first general purpose computers were built. This claim is based on the notes she appended to her translation of Menabrea’s 1842 paper, Notions sur la Machine Analytique de M. Charles Babbage. Alan Turing is often called “the father of theoretical computer science” on the basis of his 1936 article On computable numbers, with an application to the Entscheidungsproblem. Lovelace’s notes describe Babbage’s Difference Engine and Babbage’s Analytical Engine, and Turing’s paper proves the existence of a Universal Turing Machine. This talk will compare the designs and capabilities of these early theoretical computing machines.
“Reverse Mathematics, Graphs, and Matchings”
Tuesday, March 11, 2014 • Science 465 • 4:00pm • Flyer
How can we tell if two theorems are essentially the same? If we can prove that they are equivalent, then they are in some sense interchangeable. If our equivalence proof relies on a particularly small set of assumptions, then our claim of similarity is even stronger. This is the fundamental motivation of reverse mathematics, a program in the foundations of mathematics initiated by Harvey Friedman and Stephen Simpson. This talk will illustrate some results and techniques of the program.

Fall 2013

Photo of Dr. Mathew Dr. Thomas Mathew
Presidential Research Professor
University of Maryland, Baltimore County
“The Assessment of Bioequivalence: A Statistical Overview”
Monday, November 18, 2013 • MSC BE5 • 4:00pm • Flyer
The topic of bioequivalence deals with procedures for testing the equivalence of two drug products: typically, a generic drug and a brand name drug on the market. Bioequivalence testing consists of showing that the concentration of the active drug ingredient that enters the blood is similar for the two drugs. Area under the time-concentration curve, or the AUC, is usually used for this purpose, and the data are obtained based on cross-over designs. In the talk, the bioequivalence problem will be introduced, its history will be discussed, and examples will be provided. Statistical criteria that are used for bioequivalence testing, especially the criterion of average bioequivalence, will be discussed. Methodology for testing the hypotheses of average bioequivalence will be addressed. The emerging area of equivalence testing in the context of biosimilars will be briefly touched upon.
“Methodology and Some Applications”
Tuesday, November 19, 2013 • CH 105 • 4:00pm • Flyer
Standard likelihood based methods that are usually used to analyze data arising from a parametric model are typically accurate to the first order. Higher order inference procedures provide major improvements in accuracy, and are available for discrete as well as for continuous data. In the talk, two applications of higher order inference will be described. Both the applications deal with the computation of an upper tolerance limit: a limit that is expected to capture a specified proportion or more of a population with a given confidence level. The limit is constructed using a random sample, and the confidence level refers to the resulting sampling variability.

The first application that will be discussed is on the computation of tolerance limits under the logistic regression model for binary data. The data consist of binary responses, and upper tolerance limits are to be constructed for the number of positive responses in future trials corresponding to a fixed level of the covariates. The problem has been motivated by an application of interest to the U.S. Army, dealing with the testing of ballistic armor plates for protecting soldiers from projectiles and shrapnel, where the probability of penetration of the armor plate depends on covariates such as the projectile velocity, size of the armor plate, etc. The second application is on the computation of upper tolerance limits under a general mixed effects model with balanced or unbalanced data. Higher order inference procedures will be used to obtain accurate solutions in both the applications. Numerical results, examples and data analysis will also be reported.

Spring 2013

Dr. Howie Wiess
Professor of Mathematics
Georgia Institute of Technology
Photo of Dr. Weiss
“Sir Ronald Ross, the SIR transmission model and the Foundations of Public Health”
Wednesday, March 6, 2013 • Drinko 402 • 4:00pm
After some brief comments about the nature of mathematical modeling in biology and medicine, we will formulate and analyze Sir Ronald Ross's SIR infectious disease transmission model. The model is a system of three non-linear differential equations that does not admit a formula solution. However, we can apply methods of calculus to understand a great deal about the nature of solutions. Along the way we will use this model to develop a theoretical foundation for public health, and we will observe how the model yields several fundamental insights (e.g., threshold for infection, herd immunity, etc.) that could not be obtained any other way. At the end of the talk we will compare the model predictions with data from actual outbreaks.
“Perspectives on multiple waves during flu pandemics”
Thursday, March 7, 2013 • SH 511 • 4:00pm
A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves (peaks) of infections. However, the mechanisms responsible for the multiple waves are uncertain. We use mathematical models to exhibit mechanisms each of which can generate multiple waves.

The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography, etc.); each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Four of the models reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively.

We use the models to study the effects of border control and vaccination strategies on the outbreak, and we propose a hypothesis about why China only experienced a single wave of infections during the 2009 flu pandemic.
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