Spring 2017

Faculty Organizers: Professors Thomas Johnstone and Johann Thiel

Room and Time: N719, 12:45-2pm


 2  No meeting
 9  No meeting (school closed)
16  Speakers: Sandra Altman, Edward Beckenstein, and Genady Grabarnik
 23  Speaker: Satyanand Singh (12:30-1:30)
 2  No meeting
 9  No meeting
 16  Speaker: Said Antonio Kas-Danouche Rojas
 23  Speaker: Katherine Poirier
30  Speaker: Johann Thiel
 3  No meeting
 13  No meeting
 20  No meeting
 27  Speaker: Nadia Benakli
 4  No meeting
 11  Speaker: Suman Ganguli
 18  Speaker: Austin Rochford

Abstracts — Spring 2017

Date: Feb. 16, 2017
Speaker: Sandra Alitman, Edward Beckenstein, and Genady Grabarnik
Title: Graduate Mathematics at St. John’s College of Liberal Arts & Sciences
Abstract: St. John’s College of Liberal Arts & Sciences, of St. John’s University will be offering a new M.A. program in Applied & Computational Mathematics this coming Fall, 2017.
In the talk, faculty will discuss the nature of the program, its courses, faculty and opportunities the program may offer for potential careers.
There will also be a brief discussion of the nature of graduate mathematics, and what is unique about the program at St. John’s.

Date: Feb. 23, 2017
Speaker: Marieme Toure
Title: A Summer at Microsoft Research
Abstract: This talk, by one of our applied math students, will cover her summer experience at Microsoft Research on flow and efficiency in NYC’s taxi system.

Date: Mar. 16, 2017
Speaker: Said Antonio Kas-Danouche Rojas
Title: Innovation Using Mathematical Modelling and Simulation
Abstract: Over the centuries, human beings have innovated by making use of mathematics to solve problems in multiple areas of everyday life. Mathematics has varied perspectives ranging from the development of complex intellectual theories, to the modeling of phenomena that occur in our environment.
By deriving mathematical models, computer simulators are developed to capture images of complex structures. The cardiovascular system, the respiratory system, the transportation of crude through pipes, recovery of crude, among other problems, are all fractal systems. The new mathematical methodologies are the means to consider the phenomena from another perspective closer to reality, and looking for new technologies to study the stability/instability of the systems.
In 1988, at the American Mathematical Society Assembly, it was said: “Recent developments in the mathematical sciences suggest that there will be a significant potential increase in fundamental advances in life sciences, which will depend in large part on mathematical models and computing.” And this is what we have been seeing through the last years.
This lecture will explain what a modeling process is. The objective is to simulate computationally the flow behavior of two concentric immiscible fluids, which travel inside a cylindrical tube with circular cross-sections, typical in the transportation of crude through pipes. Also, insoluble surfactants are present at the interface between the two fluids. The goal is to present how to go from the setting of the real life problem to the final simulation. This simulation must be designed so that an average user of computers, or related devices, can experiment and learn from the behavior of the phenomena considered in the original problem, without the need to be a mathematician.

Date: Mar. 23, 2017
Speaker: Katherine Poirier
Title: Coming Clean about the Poincare Conjecture and Stolen Bikes
Abstract: A few years ago, Professor Poirier gave a public lecture titled “The Poincare Conjecture: Or How I Learned to Stop Worrying and Lock My Bike,” which contained a little white lie. Luckily, the audience let her get away with the lie, but she’s been feeling guilty about it ever since! In this Math Club talk, she’ll introduce the Poincare Conjecture, which was first posed in 1904 and which stumped mathematicians for the next 100 years. She’ll also tell you how it can help you lock up your own bike. And she’ll finally come clean about the lie.

Date: Mar. 30, 2017
Speaker: Johann Thiel
Title: Dynamic Programming and Memoization
Abstract: Dynamic programming and memoization are two programming techniques designed to speed up computations in some problems involving recursion (usually in exchange for higher memory usage). We will look at applications of these techniques to the Fibonacci sequence, a path-counting problem, and the coin change problem. In particular, we will create several Python scripts implementing these techniques.

Date: Apr. 27, 2017
Speaker: Nadia Benakli
Title: Perfect Code Cryptography
Abstract: In mathematics and computer science, cryptography is being used to solve different types of real life problems connected to secure communication in the presence of third parties – eavesdroppers. In this presentation, we will discuss one use of graph theory in secret communication.

Date: May 11, 2017
Speaker: Suman Ganguli
Title: Turing: From Computers to Machines
Abstract: What is computation? What is an algorithm? In 1936, a 24-year old Cambridge mathematics student named Alan Turing published a paper titled “On Computable Numbers, with an Application to the Entscheidungsproblem” which addressed these questions.
In his paper, Turing defined certain “logical computing machines”—what we now call Turing machines.  Turing machines are not physical machines, but rather a mathematical definition of algorithmic computation, based on Turing’s careful analysis of what a human “computer” does in carrying out a computation. In the ensuing decades, Turing machines became the standard model for understanding computation, formed the theoretical foundation for the first physical computers (including through Turing’s own work after World War II), and have become central to discussions in mathematical logic, theoretical computer science, philosophy of mind, cognitive science, and artificial intelligence.
In this talk, we will discuss the basic concepts of Turing’s work on computability, by examining the definition of Turing machines and working with some examples. We will also sketch Turing’s remarkable construction of a universal Turing machine: a single machine which can simulate any other machine, i.e., which can carry out any algorithm, and his proof that some questions are algorithmically undecidable, i.e., cannot be answered by any algorithm.