Rose-Hulman Undergraduate Mathematics Conference
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Dr. T. Christine Stevens Dr. Kelly Black

Dr. Christine Stevens
Department of Mathematics and Computer Science, Saint Louis University

Biography: T. Christine Stevens is Professor of Mathematics and Computer Science at Saint Louis University, where she teaches everything from pre-calculus through advanced graduate courses in topology. A graduate of Smith College, she earned her Ph.D. in mathematics at Harvard University. Her research interests are in topological groups and in the history of mathematics, but she has also spent time working for Congress and for the National Science Foundation. She is the director of Project NExT (New Experiences in Teaching), a professional development program that has thus far helped over 1000 new mathematics faculty to launch their careers.

Abstract: Everyone knows how to make a ham sandwich: You put a piece of ham between two slices of bread, and you cut it in half. If the piece of ham and the slices of bread are square, then it's easy to cut the sandwich so that each half contains exactly half of the ham and exactly half of each slice of bread. But what if the ham and the bread aren't square, or even symmetrical? What if the ham is unevenly cut, one slice of bread is in the corner of the room, and the other slice is down the hall somewhere? Using just one swing of the knife, can you still cut the sandwich so that each half contains exactly half of the ham and exactly half of each slice of bread? The Ham Sandwich Theorem says that the answer is "yes." In the course of explaining why this is true, Ill discuss some concepts from the branch of mathematics that is called topology. I will assume that the audience knows what the graph of a function is; familiarity with the idea of a continuous function is helpful, but not essential.
Abstract: Many of the issues that confront local, state, and national governments -- ranging from how to conduct a census to what to do with toxic waste and how to stimulate the economy -- have a significant mathematical and scientific component. Our success in dealing with these issues depends on our ability to integrate their technical and political aspects. How do Members of Congress -- whose background is usually in law, not science -- deal with issues like these? Drawing upon my own experience working in a Congressional office, I will offer an answer to this question. I will also explore the implications of that answer for society at large, but especially for students majoring in mathematics, science, and engineering.


Dr. Kelly Black
Department of Mathematics, Union College, Schenectady, NY

Kelly Black is an Associate Professor in the Department of Mathematics at Union College. He received an undergraduate degree in Mathematics and Computer Science from the Rose-Hulman Institute of Technology. He received a Ph.D. in Applied Mathematics from Brown University. Kelly's research interests are in the analysis and implementation of numerical techniques in scientific computing. His activities have been varied and has taken part in ecological modeling, simulation of the dynamics within a laser media, and the approximation of both compressible and incompressible flows. The one constant in these activities is the use and analysis of numerical techniques that are used to approximate the solutions to the relevant systems.

Title: Hey, You Kids! Turn Up That Noise... Kelly Black and John Geddes
Abstract: In some physical and economic systems noise can play a dominant role in the transient behavior of the system. Often times mathematical theories have been developed for the underlying distributions of the solutions for the systems but little theory is available for the transient solutions which is often the primary interest of the scientist/engineer/economist. Compounding this problem the methods used for the numerical treatment of noise are not well developed and are most often focused on additive noise. We discuss some of the background and motivation of the development of mathematical models of noise. We then examine a numerical scheme to approximate proportional noise in stochastic differential equations. Finally, the statistical properties of the numerical approximations of a simple model are examined.