2021: 17th UNCG RMSC

The 17th UNCG Regional Mathematics and Statistics Conference took place on Saturday, November 20, 2021. The 2021 conference was held virtually.

Professor Igor Erovenko conducted a professional development seminar in which he spoke about the art and science of great presentations.

The plenary lecture, Evolution as a field of mathematics, was delivered by Dr. Benjamin Allen, Emmanuel College.
Website: Benjamin Allen | Emmanuel College

Evolution as a field of mathematics

New mathematical fields arise when fundamental patterns and structures—either from the real world or from within math itself—are abstracted and formalized into definitions and axioms. Examples include calculus for the study of continuous change, group theory for the study of symmetry, or topology for the study of deformable shapes. Evolution is a ubiquitous process: it underlies all of biology and also occurs in other contexts such as human culture, technology, and language. So, what would evolution look like as a field of mathematics? As a potential starting point, I will present a framework that formalizes the fundamental building blocks of population, heritable types, replacement, and mutation. Within this framework, I will show how key concepts like neutral drift and fixation probability can be defined and mathematically analyzed. Applying this framework to social dilemmas on networks, I will show how graph structure can be a key determinant of whether cooperative behaviors can evolve.

2020

2019: 15th UNCG RMSC

The 15th UNCG Regional Mathematics and Statistics Conference took place on Saturday, November 2, 2019.

A panel discussion was held on Mathematics and Statistics careers. The panelists were Emily Griffith (NCSU), Thomas Parrish (Strix), Hristo Pavlov (Lincoln Financial), and John Stufken (UNCG).

The plenary lecture, Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles, was delivered by Dr. Brian Reich, Department of Statistics, NC State University.

Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps as well as short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally-efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.