CMS is organizing three-hour mini-courses to add more value to meetings and make them attractive for students and researchers to attend.
The mini-courses will be held on Friday afternoon, June 4th and include topics suitable for any interested parties. You don’t have to be registered for the meeting in order to register for mini courses. All times listed below are in Eastern Time.
Registration fees for the mini courses are:
Regular rate (Subject to Change) | |||
| Students/Postdocs (members) | $50 | ||
| Students/Postdocs (non-members) | $75 | ||
| CMS Members | $100 | ||
| CMS Non-Members | $150 |
Optimal transport and stochastic processes in developmental biology
Friday June 4th | 11:00 - 14:00
Presenters:Young-Heon Kim (UBC), Hugo Lavenant (UBC), Brendan Pass (Alberta), Geoff Schiebinger (UBC)
Optimal transport is a rapidly growing area of research at the intersection of probability, analysis, and optimization. It gives rise to powerful tools for comparing probability distributions and computing couplings between random variables, and it has found applications in physics, economics, statistics, machine learning and biology.
This minicourse introduces the powerful computational and analytical tools of optimal transport and demonstrates how these concepts can be applied to analyze stochastic processes in biomedical data science. In addition to a self-contained introduction to the classical theory and the modern developments, we explain how concepts from optimal transport can be applied to model a developing population of cells as a curve in the space of probability distributions. We highlight new experimental technologies, like single cell RNA sequencing, that allow us to sample from these probability distributions, and we show how to use optimal transport to recover a developmental curve from samples at various time-points. We illustrate these concepts with an interactive tutorial, using real data from stem cell reprogramming.
Speakers: There will be two main speakers of this minicourse, Hugo Lavenant and Geoffrey Schiebinger (both UBC).
No knowledge of biology or optimal transport will be required. Familiarity with elementary concepts in probability theory and optimization might be helpful.
Combinatorial Game Theory
Friday June 4th | 11:00 - 14:00
Presenters: Melissa Huggan (Ryerson), Svenja Huntemann (Concordia University of Edmonton), Richard J. Nowakowski (Dalhousie)
Combinatorial games are two player games of pure strategy with no chance devices; for example, Chess and Go. The theoretical underpinnings were introduced in the 1970s with the books “Winning Ways” and “On Numbers and Games”, and the last 20 years have seen substantial growth in the field. In this mini-course we will introduce the main tools used in the study of the three main types of games (impartial, all-small, and hot games), and give an overview of past and current research trends.
Required mathematical background: an introductory course in discrete mathematics would be an asset. No combinatorial game theory background is required.
Tools and Techniques for Modelling and Analyzing Complex Networks
Friday June 4th | 11:00 - 14:00
Presenters: Francois Theberge (Tutte Institute and Ottawa)
In this 3-hour course, a subset of the following topics will be covered:
– introduction to complex networks
– models for random graphs
– small world phenomenon and benchmark models
– graph clustering algorithms
– comparison of graph embeddings
The material is based on the presenter’s own current research as well as two recent books by Barabasi (Network Science) and Latora, Nicosia and Russo (Complex Networks).
The mini-course is designed for a general math audience, including students and practitioners. The topic is applied, and an introduction to the terminology and concepts from graph theory and probability will be provided, as required.
Support Strategies for Math and Stats Students
Friday June 4th | 11:00 - 14:00
Presenters: Dr Ana-Lucia Vargas Sandoval, MSc Paula Beukers
Strategies for Supporting Mathematics and Statistics Students in Higher Education
During this 3-hour session, we will share effective strategies from fellow math teachers that help you better engage and support your students. We will be diving into some pedagogical theory, concrete case studies and practical take-aways. Next to this, we will also show you how the Bolster Academy learning tool can help you implement these strategies in your classroom.
Topics we will cover:
- Mastery based learning
- Guided discovery
- Adaptive learning
- Technical demonstration of the Bolster tool.
After the session, all attendees get free access to Bolster Academy for one semester.
An Introduction to Programming in Maple
Friday June 4th | 11:00 - 14:00 Complimentary Admission
Presenters: Paulina Chin, Software Architect, Maplesoft
In addition to being an interactive environment for problem-solving, visualization, and technical document preparation, Maple also features a powerful programming language that is especially useful for working with mathematics. Becoming familiar with the Maple language will allow you to increase the range and efficiency of what you can do in Maple, from writing short scripts to automating a repetitive calculation, to creating interactive applications for students and developing new algorithms to advance your research.
This course will begin with an overview of the Maple software package, highlighting a number of its important features. We will then cover the basics of the Maple language, common data structures, and writing simple programs. We will also look briefly at tools to aid the construction of larger programs and packages and the building of interactive applications in the style of Maple’s Math Apps.
Newcomers to Maple, as well as experienced users who would like to learn more about the Maple language, are welcome.
Career Diversity in Mathematics
Friday June 4th | 15:00 - 18:00
Complimentary Admission
Presenters: Megan Dewar and David Thomson (Tutte Institute for Mathematics and Computing & Carleton University)
This 3-hour session will give students the opportunity to learn about a diversity of mathematical careers and expand their mathematics networks. With time allotted to hear from panelists who work in academia, industry and government, as well as a keynote talk by Dr. Mary Lynn Reed, students can expect to broaden their concept of what a career in mathematics looks like and gain new perspectives on the many paths such a career may take. In addition, significant time will be allotted for participants to meet, mingle and direct questions to panelists in an informal and inviting atmosphere.
About the keynote speaker: Dr. Mary Lynn Reed’s career has lead her from academia, to government, to industry … and back again. After completing her doctorate in representation theory in 1995, Dr. Reed obtained a faculty position but “it was not her dream job”, and she soon began working with the National Security Agency. In 2000, she left NSA and moved to San Diego to work in the software industry, but returned to work in the intelligence community after the September 11 attacks in 2001. In 2016 she became Chief of Mathematics Research at NSA. Most recently she has returned to academia, becoming Head of the School of Mathematical Sciences at the Rochester Institute of Technology. Dr. Reed also holds an MFA in creative writing.
Mathematical Modelling Of Real-World Infectious Disease Epidemics – An R Based Hands-On Mini Course
CANCELLED
Presenters: Ashok Krishnamurthy (Mount Royal University)
Mathematical modelling of infectious diseases is an interdisciplinary area of increasing interest. In this mini course we describe and illustrate an understanding of infectious diseases and its value for public health. The mini course will be based on our real-world experience of tracking the spatial spread of measles in pre-vaccine England and Wales (1944-1966), and Ebola in the Democratic Republic of Congo (2018-2020) using partial differential equations.
This interactive mini course will be delivered using real-world data and practical simulation exercises using the free, open-source software R. Participants will learn to build a compartment model of epidemiology (SIR, SEIR, SEIRD etc.) to track the spatial spread of an infectious disease outbreak. Examples will include a realistic scenario involving tracking of the novel Coronavirus (COVID-19).
This mini course will be designed for early-career data scientists, mathematicians, new PhDs, and graduate students. No prior detailed knowledge of modelling infectious diseases or epidemiology is required.
Teaching, learning, and doing math online just got easier! Maple Learn is a revolutionary new online environment for math education that combines an intuitive environment with the mathematical power of Maple, the world-leading math software from Maplesoft. And with free entry-level accounts, you and your students can start using Maple Learn right away!
Join us and experience Maple Learn for yourself.
You’ll learn how to:
- Show the exact level of detail you want in a calculation, from immediate answers to fully worked solutions
- Graph curves instantly, and watch them change in real-time as you change your expression
- Parameterize expressions at the click of a button, for easy concept explorations
- Place plots, text, and math anywhere you wish, putting side calculations beside your main derivation, and generally treating the canvas like you would a piece of paper
- Share documents with others, even if they don’t have a Maple Learn account