Two Weeks at WATERLOO - A Summer School for Women in Math
The Summer School for Women in Mathematics is a two-week residential mathematics enrichment program for outstanding senior female undergraduates. The school will be held from August 10-23, 2014 at the University of Waterloo in Waterloo, Ontario, Canada. Up to 16 students will be selected to participate in the program.
Our goal is to encourage these gifted young women to continue on to graduate work in mathematics. The program will provide both enrichment of the undergraduate curriculum and a research component, in a collaborative environment.
The summer school is open to female undergraduates studying mathematics or a related discipline at a Canadian university. Preference will be given to senior students with at least one year of studies remaining in their program. Canadians and permanent residents studying outside Canada are also eligible to apply. Each participant will receive on
campus room and board and an allowance for travel expenses from within Canada.
The program will run from August 10-23, 2014.
There will be two mini-courses, running through the two weeks, taught by female instructors on the topic of their choice. One course will meet in the mornings, the other in the afternoons. The students will do group projects with presentations on the last day for each course. Each course will also have a graduate student TA/mentor. The instructors and courses planned for 2014 are as follows.
"Kakeya sets, or, a handbook of parallel parking", taught by Professor Malabika Pramanik from the University of British Columbia.
Course description: We will begin with a discussion of Kakeya-Besicovitch sets and some basic constructions of such sets in the plane. A brief history of the study of these sets and their many applications will be provided. By the end of the course, we hope to get to some modern treatments involving the construction of two-dimensional random Kakeya-type sets using percolation on trees, and their consequences in terms of a characterization of the boundedness of directional maximal operators in the plane.
"Algorithmic learning theory", taught by Professor Jennifer Chubb Reimann from the University of San Francisco.
Course description: Machine learning is a extremely important part of one of the most exciting new research areas in artificial intelligence, mathematics, and statistics: data science. In this course we'll talk about a basic framework for machine learning and see how it works.
Using an intuitive approach based on the idea of a child trying to
learn a language, we'll start by formulating a basic formal learning
paradigm, including what we mean by "language" and "learner." A
careful consideration of what it means to say a learner has "learned"
a language will lead us to consider different types of identification,
locking sequences, and learning strategies. Along the way, we'll
cover some of the basics of computability theory including Godel
coding, recursive and recursively enumerable sets and functions, and
some of their properties. We'll talk about variations and alternative
learning paradigms as time allows (and in some of your projects!).
There will be four female guest speakers who will give a presentation related to their work and will talk about their life/work experiences. We are pleased to announce that Professor Mary Lou Zeeman from Bowdoin College will be giving a distinguished public lecture, and Professor Habiba Kadiri from the University of Lethbridge has also confirmed her attendance as a guest speaker.
One day each week will be for field trips to businesses employing mathematicians. We anticipate spending one of these days in Waterloo and a second in Toronto. Additionally we will have an excursion to Niagara Falls on the middle weekend of the summer school.
Apply online. The application deadline is January 31, 2014 although early applications are encouraged. We expect to notify successful applicants in early March 2014.
For any questions about the summer school or the application process please contact firstname.lastname@example.org.