Department of Mathematics

Fall 2017 Seminar Series

other semesters

Seminars are at 12:00pm at RLC 200

WED 09/06/17:

WED 09/13/17: Rocco Pascale, Greg Zajac, Taylor Salkoswky -- Grad Student Internship Report

ABSTRACT: Students in the Master’s Program in Applied Mathematics– Data Analytics talk about their 2017 summer internships at 1010Data, The New York State Energy Research and Development Authority (NYSERDA) and Black Rock.

WED 09/20/17: Meghan Makarczuk, Center for Career Development Manhattan College, 'π-Lingual' Careers for Math Majors

ABSTRACT: A career in math can take many shapes and forms. The study of mathematics provides an excellent preparation for careers including data science, education, business, including management and finance, the actuarial field, operations research as well as serving as a great foundation for other professions such as medicine, law and public health. Whether you are new to Manhattan or just looking to get a jumpstart on your career, this talk will give you the rundown on how to explore various opportunities to achieve personal and professional goals. Topics covered will include: internships and jobs, resume building, interviewing, networking, and negotiating salary.

WED 09/27/17:

WED 10/04/17:

WED 10/11/17: Anthony Depinho, "What's in the Air? Air Quality, Mathematical Models and my Summer Research at Harvard"

ABSTRACT: The National Science Foundation hosts summer research experiences for undergraduates at institutions across the country in a variety of STEM disciplines. This summer I had the opportunity to conduct research at Harvard University at the Institute for Applied and Computational Science, as part of a team of undergrad researchers sponsored by the School of Public Health. We undertook a 10-week project during which we trained statistical models for three EPA air pollutants - NO2, SO2, and PM2.5 - and applied the models to produce intra-urban pollution variation in the Greater Boston Area. This talk will address the main components of our work: data collection, introduction to two specific statistical models, model optimization, and data visualization. Further, this talk will address the research experience more generally and in regard to takeaways from my time there that are non-technical, but equally as important. The talk will be accessible to faculty and students of any level.

Anthony Depinho is a Junior Mathematics major at Manhattan College

WED 10/18/17: Samanthan Morrison, "Building a Mathematical Model for Lacrosse Analytics"

ABSTRACT: This summer, I conducted research as a Jasper Summer Research Fellow with Dr. Helene Tyler in the Manhattan College Department of Mathematics. My project was creating a mathematical model in order to describe the style and performance of the Manhattan College Women’s Lacrosse team. The model used Probabilities, Linear Algebra, and Network Theory. The analysis was done focusing on questions from student athlete’s, with the hope of providing information to the team that could be used to improve their performance in the areas of midfield transitions, shots on goal, and clears. My talk will focus on data collection, methods of analysis, and the final results and their interpretation.

Samanthan Morrison is a Junior Mathematics major at Manhattan College

WED 10/25/17: Dr. Angel Pineda , Manhattan College, "Mathematicians without Borders: The Committee for Developing Countries of the International Mathematical Union"

ABSTRACT: The world has large untapped potential for mathematical talent which could be developed with support for mathematics education and research. The Commission for Developing Countries (CDC) of the International Mathematical Union (IMU) works to support mathematical efforts in the developing world through programs such as the Volunteer Lecturer Program (VLP), the Graduate Research Assistantships in Developing Countries (GRAID) Program and conducting studies on mathematical activity to identify areas where modest support could have large impact. In this talk, I will share my experiences, first as a volunteer lecturer in Cambodia teaching graduate numerical analysis, then studying mathematical development in Latin America and finally as a member of the CDC. I will also share current opportunities to get involved with supporting mathematics in the developing world.

WED 11/01/17:

WED 11/08/17: Dr. Richard Gustavson, Manhattan College , " An Introduction to Elimination Theory "

ABSTRACT: A vital tool in the study of systems of multivariate linear, polynomial, or differential equations is the ability to eliminate variables, resulting in consequences of the system that only depend on the non-eliminated variables. Equally as important is the complexity of the resulting consequences, which can determine how much time must be spent computing the consequences or even if they can be feasibly used in practice. In this talk I will present elimination techniques for linear, polynomial, and differential equations, and demonstrate the similarities and differences of the various techniques and their complexities.

WED 11/15/17: Dr. Richard Goldstone, Manhattan College , " Some remarks about Euler’s Prime-producing Polynomial"

ABSTRACT: Leonhard Euler noticed in 1772 that if we substitute consecutive integers 0, 1, 2, 3,. . . into this polynomial, we get 41, 43, 47, 53, 61, 71, 83, 97, 113, 131, 151, 173, 197, 223, 251, 281, 313, 347,. . . all of which are—so far—prime, and the exclusive prime production goes on for a while longer. In this talk, we give this result some mathematical context and explore some of its connections with undergraduate algebra. Knowledge at the level of Algebra II will be edifying for both student and faculty listeners, but there are appealing things in the talk for anyone with an interest in mathematics.


WED 11/29/17:

WED 12/06/17: Tenzin Kalden, AMDA Grad Student

ABSTRACT: Classic examples of classification employ first and second order statistics; however, adding higher order statistics may improve the accuracy of a classifier. We present a binary classifier which includes kurtosis in order to make predictions more precise. We also give experimental evidence to justify this claim.