Spring 2021
Tuesday, February 23rd
Professor Jeff Forrester and Professor Tracy McKay - Department of Mathematics & Computer Science, ºìÐÓÖ±²¥app and
Annie Kondas, Center for Advising, Internships and Lifelong Career Development, ºìÐÓÖ±²¥app
"Where Do I Go From Here?"
In this chat we discuss a wide variety of careers and opportunities for students majoring in mathematics and computer science. In addition, we talk about graduate school options, internships, and REUs (Research Experience for Undergraduates).
Noon
Via Zoom (link was emailed to Math & CS majors)
Friday, April 16th
Dr. Brian Allen, Assistant Professor of Mathematics at University of Hartford
"Ironing Out the Wrinkles in a Black Hole Horizon"
The heat equation is a partial differential equation (PDE) which is used to model the flow of heat through a medium. In geometric analysis this equation has inspired many other heat like equations where there is an analogy between heat and curvature. In this talk we will build intuition for the heat equation, see how this equation can inspire geometric evolution equations for surfaces, and then explore a surprising connection between the evolution of surfaces and the relationship between the event horizon of a black hole and its mass.
12:30pm
Via Zoom (link will be emailed to Math & CS majors)
Tuesday, April 20th
Mathematics & Computer Science Majors Dinner
Upsilon Pi Epsilon and Pi Mu Epsilon Honor Society Inductions
Departmental Prizes and Awards
8:30pm (Eastern time)
Zoom link will be email to majors closer to event date
Friday, May 7th
Beverley-Claire Okogwu Honors Defense
"Chaos Genetic Algorithms vs Genetics Algorithms: Why the Distributions of Mutation Sizes Matter"
Genetic Algorithms (GAs) are a branch of search algorithms popularly used due to their ability to find near-optimal solutions in reasonable amounts of time. The algorithm itself stems from the Darwinian Theory of natural evolution. Within the GA, much research has been done to discover ways by which its performance can be improved. One direction of research aims at replacing the randomly generated numbers used in creating the initial population/solution, crossover, and mutation with a chaotic mapping. In addition, other papers report on ways that evolution can improve performance by self-adapting the mutation and crossover operators over time.
In this paper, the distribution of values generated by chaotic maps are investigated to explain the performance differences between the Chaos Genetic Algorithm (CGA) and the standard Genetic Algorithm. Of particular interest is the use of the chaotic map parameter values used in the CGA’s mutation that produce explorative or exploitative distributions of values. We propose the hypothesis that it is these distributions of values used for mutations, rather than the chaotic properties of the maps used, that explain differences in performance. A model and set of experiments that investigate this hypothesis are proposed and carried out. The same model is used to investigate the possibility of self-adaptation of chaotic map parameters.
Based on the results of the investigation of the chaotic map distributions, the distributions of mutation sizes can indeed explain the behavior of the CGA relative to the GA. In addition, experiments show that self-adaptation of the chaotic map parameter is possible and consistent with the observed effects of mutation size distributions.
3:00pm (Eastern time)
Via Zoom
Majors will be emailed the Zoom link closer to event date
Wednesday, May 12th
Mathematics & Computer Science Seniors Reception
The professors in the Department of Mathematics and Computer Science invite you to an outdoor in-person graduation celebration. We look forward to congratulating you for all of your hard work and to wish you well as you embark on your life as Dickinson alumni.
4:00-6:00pm
Tome Back Patio (by outdoor classroom)
Rain Location: Morgan Field Tent