Student Talk Series: Pamela Gorkin, Bucknell University; October 23rd @ noon, Olin 268


Title: The World’s Greatest Game

Abstract: Analogy pervades all our thinking, our everyday speech and our trivial conclusions as well as artistic ways of expression and the highest scientific achievements.” This quote, from George Pòlya, is from his book, “How to Solve It”. If you have a problem that you want to solve, this is the book and, possibly, the talk for you. We’ll follow Pòlya’s four-step method as we discuss questions from different areas: probability, geometry and life and, as a plus, we’ll learn to visualize something people often think is imaginary.

Bio: Pamela Gorkin is a Professor in the Mathematics Department here at Bucknell.

Student Talk Series: Allison Gibson, Nielsen Holdings; October 2nd in Olin 268 @ noon


Title: Twitter’s Influence on TV Ratings, And Other TV Mathematics

Abstract: Nielsen’s TV Ratings are used as the currency with which key business decisions are made across the media industry. What TV programs remain on the air, which advertisements you see during commercial breaks, and when you are able to watch your favorite shows on Hulu are all questions answered by analyzing ratings and other Nielsen data. Recent technology has rapidly changed how and when people watch TV, thus quickly increasing the demand for enhanced data to give TV Networks and Advertisers a thorough understanding of the current TV landscape. Using various forms of TV Mathematics, exciting new conclusions, like whether Twitter influences TV Ratings, can be made.

Bio: Allison Gibson, an Associate Media Analytic Consultant at Nielsen, graduated from Bucknell in May 2013 with a double major in Mathematics and Italian Studies and a minor in Film Studies. While at Bucknell, she was a Tour Guide and Tour Guide Student Coordinator, Co-Founder and Musical Director of The Offbeats A Cappella group, and Vice President of Food (Public Relations) for the Mathematical Association of America. Allison has been working at Nielsen, a Fortune 500 Company known for the Nielsen TV Ratings system, since graduating about a year and a half ago. She started her career as a Research Analyst providing a wide variety of media-related data to many of Nielsen’s TV, Advertiser, and Agency clients. As of August of this year, she is an Associate Media Analytic Consultant working primarily with clients like FOX Networks, AMC Networks, Tech companies (i.e. Amazon, EBay) and the Telecom sector (i.e. Verizon, AT&T).

Student Event: A Mathematical Mandala with Nancy Cleaver; October 4, @1pm, Smith Quad

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Artist Nancy Cleaver has created a (very) large mathematical mandala for the Bucknell University Mathematics Department. Her mandalas contain words, symbols or numbers. Please help us color the mandala or just stop by to find the hidden object and lend us some moral support. We’ll provide the chalk and the sidewalk. All we ask is that you color within the lines. Hope to see you there!


Distinguished Visiting Professor Talk Series: Nema Dean, University of Glasgow, September 17th @4pm in Olin 372


Title: Identifying Boundaries in Spatial Modelling for Disease Mapping

Abstract: The aim of disease mapping is to estimate the spatial pattern in disease risk across a set of areal units, in order to identify units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate disease risk, but these methods cannot identify the geographical extent of spatially contiguous high-risk clusters of areal units. We propose a two stage approach, which first produces a set of potential cluster structures for the data and then chooses the optimal structure by fitting an extension of the Bayesian hierarchical model. The first stage uses a hierarchical agglomerative clustering algorithm, spatially adjusted to account for the neighbourhood structure of the data. This algorithm is applied to data prior to the study period, and produces a set of n potential cluster structures. The second stage fits a Poisson log-linear model to the data, in which the optimal cluster structure and the spatial pattern in disease risk is estimated via a Markov Chain Monte Carlo (MCMC) algorithm. After assessing the methodology with a simulation study, it was applied to a study of respiratory disease risk in Glasgow, Scotland, where a number of high risk clusters were identified.