Canadian Mathematical Society, Ottawa, ON K1G3V4
613-733-2662 ext 733


CMS COVID-19 Research and Education Meeting (CCREM)

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, before the public lecture, 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. 

Registration fees for the mini courses are: 


Regular rate (Subject to Change)

Students/Postdocs (members)


Students/Postdocs (non-members)


CMS Members


CMS Non-Members


A Data Method for Qualitative Dynamics

Friday June 4th |

Presenters: Tanya Schmah (Ottawa) and Cristina Stoica (Wilfrid Laurier)

Numerical integration has long been used to explore dynamical systems (ODEs, PDEs and more). Advances in data science have made possible new ways to visualise dynamics and detect dynamical features and bifurcations. We will introduce a particular method for visualising the ergodic quotient of a dynamical system, based on work by Budišić & Mezić in combination with recent methods for nonlinear dimensionality reduction and clustering. We will present case studies from celestial mechanics and epidemiology, using a new Python package.

Participants should have a background equivalent to at least two differential equations courses and two real analysis courses. In order to run examples, participants should bring their own laptop with a pre-installed Python / Jupyter environment such as Anaconda. 

Career Diversity in Mathematics

Friday June 4th | 1pm - 4pm
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.

Combinatorial Game Theory

Friday June 4th |

Presenters: Melissa Huggan (Ryerson), Svenja Huntemann (Carleton), 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.

Optimal transport and stochastic processes in developmental biology

Friday June 4th |

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.

Tools and Techniques for Modelling and Analyzing Complex Networks

Friday June 4th |

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.

STUDC Mini-Course: Mathematical Communication

Friday June 4th |
Complimentary Admission

Presenters: Sébastien Lord (Ottawa), Kaveh Mousavand (UQAM), Adèle Bourgeois (Ottawa), Asmita Sodhi (Dalhousie)

This mini-course on mathematical communication is focused on three aspects: presenting mathematical ideas and results, writing academic articles, and writing grant proposals.

While this mini-course is organized by the Student Committee, it is open to all participants.

Mathematical Modelling Of Real-World Infectious Disease Epidemics – An R Based Hands-On Mini Course

Friday June 4th |

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.