Mathematics & Computer Science
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Date: 9/11/2007 (Tuesday)
Title: The Mathematical Art of M.C. Escher
Abstract: The graphic art of M.C. Escher (1898-1972) has been celebrated by mathematicians for its compelling visualizations of deep mathematical ideas. Escher's work included vivid explorations of geometric spaces, perspective, symmetry, transformation, infinity, and even logic. We will examine several works to trace the mathematical lines of thought that Escher wove into these artistic "discoveries," revealing the fundamental unity of spirit between art and mathematics that has been claimed by many great artists, including Albrecht Durer and Leonardo Da Vinci.
Bio:Dr. B. Sidney Smith is an assistant professor of mathematics at Radford University. A Colorado native, he attended Central Washington University and Cambridge University before earning his PhD at the University of Colorado, Boulder. His chief professional interests are mathematical logic and the philosophy of mathematics.
Date: 10/4/2007 (Thursday)
Title: What Linear Algebra, Mars Rovers and Brain Scans have in Common -- the Power of Approximation
Abstract: Just what is linear algebra used for anyway? To name a few areas, it is used in image compression, noise reduction in signals, PDA technology, and more. In all of these areas, it is the approximation that makes it powerful. Mathematical approximations appear in many areas of mathematics. For example, using Newton's Method to estimate a root, or using linear approximation to estimate certain values are two such approximation methods in calculus. This talk will include specific examples of the power of approximation in linear algebra. You might see pictures of Mars, you might hear voices, you might be inspired to neaten your handwriting, you might see how multi-linear algebra could be used in the medical community. Come and find out what all the excitement is about!
Bio:Carla D. Martin is an Assistant Professor of Mathematics at James Madison University. She received her PhD in Applied Mathematics from Cornell University and was an undergraduate at Virginia Tech. Her research area is numerical
linear algebra, specifically dealing with higher-order tensor decompositions. She is a violinist and has recently worked on trying to find linear recurrence relations in musical works. She is an avid rock climber and enjoys playing with her 4-year old son and 10-month old daughter.
Date: 11/1/2007 (Thursday)
Title: The Importance of Integrating Knowledge of Biology, Statistics, and Computer Science in High-Throughput Genomic Research
Abstract: High-throughput genomic technologies are increasingly being used in science and industry to identify therapeutic targets and risk-factors for specific diseases. Examples of high-throughput genomic technologies include microarrays that detect gene expression, protein expression, single nucleotide polymorphisms, and methylation. For each biological sample, thousands of measurements are obtained on a single biological specimen, so that those analyzing the resulting data are required to have a strong background in both statistics and computer science. Additionally, analysts having some familiarity with biology and molecular biology are an asset to research teams. Analysts working with such data are often called bioinformaticians. In this talk, I will discuss a specific research project currently underway to demonstrate a mathematician's contribution to research involving human health where high-throughput genomic technologies are used.
Bio:Dr. Kellie Archer earned her B.A. in mathematics at Franklin College in Franklin Indiana in 1991 and her Masters in Applied Statistics from the Statistics Department at The Ohio State University in 1993. After working full-time as an applied statistician for 4 years, she returned to The Ohio State University and received her Ph.D. in biostatistics from The School of Public Health in December, 2001. She has been on the faculty in the Department of Biostatistics at Virginia Commonwealth University since 2002. Her research interests include statistical methods for the analysis of data arising from high-throughput genomic experiments and supervised learning/data mining methods.
Date: 11/29/2007 (Thursday)
Title: Abstraction In Action: Maze Runners, Lying Oracles, and Game Theory
Abstract: In mathematics, as in life, problems that appear to be very different on the surface can turn out to be the same on a deeper level. Abstraction is the process by which we ignore irrelevant details, and focus on the underlying essentials of a problem. Abstraction is a powerful tool in mathematics, as in life, because it allows us to see relationships that might otherwise remain hidden.
In this talk, I will illustrate the power of abstraction by considering two problems from the mathematical field of Game Theory. In the Maze Runner Game, the goal is to predict the path of a runner through a maze. In the Lying Oracle Game, the problem is to detect when an untrustworthy oracle is lying. We will see that these two games are fundamentally the same, and that the correspondence between them gives new insights into their solutions.
Bio:Dr. Marcus Pendergrass received his Ph.D. in Applied Mathematics from the University of Alabama system in 1994. He is the inventor or co-inventor on nine patents, in the areas of signal processing, coding theory, and wireless communications. He is currently Visiting Assistant Professor of Mathematics at Hampden-Sydney College. When he is not doing mathematics he enjoys playing jazz piano.
Date: 1/29/2008 (Tuesday)
Title: The U. S. Census and Pierre François Who?
Abstract: In the mid-19th century, a Belgian mathematician made a remarkably accurate predication of the population of the United States 100 years later, based on the early census data and a mathematical model. Was he just lucky, or is this an example of what the physicist Eugene Wigner called "the unreasonable effectiveness of mathematics"?
Bio:David Smith retired in 2002 after 40 years on the faculty of Duke University. His degrees are from Trinity College (BS, 1958) and Yale University (PhD, 1963), and he is a Fellow of the American Association for the Advancement of Science. He is the author or coauthor of several textbooks and about 100 papers in abstract algebra, combinatorial theory, mathematical psychology, numerical analysis, and mathematics education. From 2000 to 2006 he was the Founding Editor of the Journal of Online Mathematics and its Applications (joma.org), and he is currently coauthoring (with Lawrence Moore) the second edition of Calculus: Modeling and Application, which will be an entirely online book (www.math.duke.edu/education/calculustext/).
Date: 2/21/2008 (Thursday)
Title: Modeling of Hormone Feedback Networks: Understanding Endocrine Oscillations
Abstract: Hormone secretion patterns are determined by the frequency of secretion events, the amount secreted, and the length of time the secretion event lasts. They encode messages for the target cells that control vital physiological processes, and an alteration of a secretion pattern may impede one or more of these processes. Understanding hormone secretion and developing the capability to recognize both normal and pathological patterns of hormone production is of utmost importance for establishing medical diagnoses, initiating treatment, and assessing the effects of treatment. However, it is generally impossible to collect data directly from the endocrine glands, where the hormones are secreted. Instead, information about hormone secretion patterns has to be inferred from data representing the hormone concentration in the blood where distortions, due to binding, excretion and/or biotransformation, begin immediately after the hormones enter the bloodstream.
This talk will focus on mathematical models aimed at quantifying various aspects of this problem. We will examine luteinizig hormone (LH) secretion patterns and implications leading to female infertility as well as questions related to the structure of the growth hormone (GH) network.
Bio:Raina Robeva is an Associate Professor and Chair of the Department of Mathematical Sciences at Sweet Briar College in Virginia. She holds a B.S. in mathematics and a M.S. in probability and statistics from the University of Sofia, Bulgaria, and a PhD in mathematics from the University of Virginia. Robeva has broad research interests covering fields in both pure and applied mathematics. Her work in theoretical mathematics has focused on the Markov property of random fields, and the problem of spectral synthesis in certain function spaces. Her applied projects are related to developing mathematical models for the life sciences. Robeva's research and educational activities have been and are currently supported by grants from the National Science Foundation, The National Institutes of Health, the Thomas F. and Kate Miller Jeffress Memorial Trust, the Commonwealth Health Research Board of VA, and the Carilion Biomedical Institute. Robeva is also the lead author of the book "An Invitation to Biomathematics" published in 2007 by Academic Press.
Date: 3/25/2008 (Thursday)
Title: Beautiful Images from Some Simple Formulas
Abstract: We will look at some interesting pictures that arise from coloring contour maps of functions of two variables as well as from iterating functions in the complex plane. We will especially concentrate on the wildly unexpected images that come from discontinuous functions. We will explain how to generate the images (easy) and attempt to explain why they look the way they do (hard).
Bio: Brian Heinold is an assistant professor of mathematics at Mount St. Mary's University. He received his PhD from Lehigh University in 2005. His research interests mostly center on graph theory, but he has worked on fractals and other mathematical pictures since his days as an undergraduate. When he's not doing mathematics, he enjoys hiking and gardening.
Date: 4/17/2008 (Thursday)
Title: Methods of Multiple Comparisons in Statistics
Abstract: In this talk, we will start with the basic methods of comparing two normal means and proceed to comparing multiple means and orthogonal contrasts. Many common methods such as Bonferroni, Scheffe, Tukey, Dunnett and Hsu will be presented. Recommendations will be given for when and under what conditions each one of these is preferred. Extensions to multivariate means will also be presented.
Bio: Hasan Hamdan graduated from Birzeit University in Palestine with a Bachelor of Science degree in mathematics in 1993. He came to American University as an exchange graduate student in 1994 and graduated with a Master's in mathematical statistics in 1996. In 2000 he earned a PhD in statistics at the American University under the supervision of Dr. John Nolan. He is a 2003 National and Section NExT fellow and just finished serving as an MAA MA/DC/VA Section officer (Fall 2007- Spring 2008). Currently he is an associate professor in the Department of Mathematics and Statistics at JMU where he is very involved with undergraduate teaching and research. He is married with 2 beautiful children (a 5-year old daughter Jenine and a 4- year old son, Adnan, and his wife is a graduate student at JMU). He loves playing with his children and in the process has become a dinosaur expert.