Category: Talks

Title: Lattice paths between two boundaries

Abstract: Dyck paths are lattice paths with steps N=(0,1) and E=(0,1) starting
at (0,0), ending at (n,n), and never going below the diagonal y=x.
Among the many interesting facts known about Dyck paths, one is that
the parameters ‘number of E steps at the end’ and ‘number of returns
to the diagonal’ have a symmetric joint distribution, meaning that to
each Dyck path we can bijectively associate another one where these
parameters are interchanged.

I will show that this symmetry property applies not only to Dyck
paths, but more generally to lattice paths with N and E steps that lie
between any two fixed boundaries. Finally, I will discuss how these
lattice paths are related to other objects in combinatorics, such as
matroids, semistandard Young tableaux and k-triangulations.

This is joint work with Martin Rubey.

Title: Learning from systematic descriptions of mathematics professional development

Abstract:  In this talk I present emerging results from a systematic
review of publications on mathematics professional development. The
research team reviewed over 170 papers published between 1992 and 2010
to address the following research questions: What do we know about the
design and conduct of mathematics PD being studied by researchers and
how are these PD characterized in research reports? The conceptual
framework included six elements: theory, context, goals, content,
format, and activities.

Title: Random Groups at Density 1/2

Abstract: Random groups in the density model (with density 0<d<1) have
presentations with a random set of (2m-1)^{dk} relators of length k on
m generators. The classic theorem in the density model states that for
d>1/2, random groups are asymptotically almost surely trivial or
isomorphic to Z/2Z, while for d<1/2, random groups are asymptotically
almost surely infinite hyperbolic. This summer our research group
studied random groups at d=1/2 and found both infinite hyperbolic
groups and trivial groups are generic, depending on how we tuned
certain parameters.

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.

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).

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.

Title:  How many groups?  Visual tools for assessing cluster structure in data.

Abstract: Cluster analysis is a set of methods designed to explore and find unknown group structure in (multivariate) data. There are a wide variety of methods available, the application of which can result in many different proposed cluster structures. Particularly for more heuristic methods (e.g. hierarchical clustering), it can be difficult to make an objective decision on which method/number of clusters gives the “best” answer. One alternative that claims to address this flaw is the model-based clustering methodology – the application of, often Gaussian, mixture models usually with some likelihood based criterion for selection of the best model/number of mixture components. An issue with this approach can be its tendency to overestimate the number of groups when associating each mixture component with a cluster (estimated group). This talk seeks to present novel applications of an old fashioned clustering tool – the dendrogram – to visually assess either combination of mixture components into clusters or to assess the similarity/grouping of clustering solutions from a variety of methods (applied to the same data). The dendrogram’s tree diagram presentation is a particularly useful graphic as it can be easily understood by laypeople and is a visually appealing tool for exploratory analysis into group structure. It is also a good summary for data of arbitrary dimension.