For inquiries about enrollment for fall/winter 2020 term courses, please contact the course instructors directly.

PIMS is pleased to announce four new network-wide graduate courses in mathematical sciences! These courses provide remote access to experts across the PIMS network. Students at PIMS Canadian member universities can get graduate credit via the Western Deans Agreement. Be advised, in some cases, students must enroll 6 weeks in advance of the next term.

Please complete the sign-up form below to receive more information on these courses.

Expand each item to see a course abstract

The Fall 2020 offering of Math 827, Graph Theory will consist of three units on advanced graph theory topics.

The first unit will be 6 weeks will be on algebraic techniques in graph theory taught by Dr. Karen Meagher of the University of Regina. The focus will be on spectral graph theory, adjacency matrices and eigenvalues of graphs. We will consider important families of transitive graphs where algebraic methods are particularly effective.

The second unit will be 3 weeks on Cayley graphs, taught by Dr. Joy Morris from the University of Lethbridge. This unit will focus on automorphisms, isomorphisms and the isomorphism problem, and Hamilton cycles, all in the context of Cayley graphs.

The third unit will be 3 weeks on the topic of random graphs taught by Dr Karen Gunderson from the University of Manitoba. This unit will cover various models of random graphs and some types of pseudorandomness.

This course will give students an overview of Non-Newtonian Fluid Dynamics, and discuss two approaches to building constitutive models for complex fluids: continuum modeling and kinetic- microstructural modeling. In addition, it will provide an introduction to multiphase complex fluids and to numerical models and algorithms for computing complex fluid flows.

- Introduction
- Background and motivation
- Review of required mathematics

- Continuum theories
- Oldroyd’s theory for viscoelastic fluids
- Ericksen-Leslie theory for liquid crystals
- Viscoplastic theories

- Kinetic-microstructural theories
- Dumbbell theory for polymer solutions
- Bead-rod-chain theories
- Doi-Edwards theory for entangled systems
- Doi theory for liquid crystalline materials

- Heterogeneous/multiphase systems
- Suspension theories (Einstein, Taylor, Batchelor, etc.)
- Kinetic theory for emulsions and drop dynamics
- Energetic formalism for interfacial dynamics
- Numerical methods for moving boundary problems

- Applications
- Polymer processing
- Sedimentation and Fluidization
- Bio-materials and processes: Pattern formation and self-assembly
- Others (gels, surfactants, colloids, Marangoni flows, etc.)

This course is part of a long-term initiative to develop integrated teaching and
learning optimal transport infrastructure connecting the various PIMS sites. The
plan is to offer this course several times over the next few years; in each
iteration, ‘X’ will be chosen from the many disciplines in which optimal
transport places an important role, including data science/statistics,
computation, biology,finance, etc. **In Fall, 2020 we will take
‘X’=“economics”.**

This course has two main objectives: first, to introduce a wide range of students to the exciting and broadly applicable research area of optimal transport, and second, to explore more closely its applications in a particular field, which will vary from year to year (represented by ‘X’ in the title). Optimal transport is the general problem of moving one distribution of mass to another as efficiently as possible (for example, think of using a pile of dirt to fill a hole of the same volume, so as to minimize the average distance moved). This basic problem has a wealth of applications within mathematics (in PDE, geometry, functional analysis, probability…) as well as in other fields (comparing images in image processing, comparing and interpolating between data sets in statistics, matching partners in economics, aligning electrons in chemical physics…) and is currently an extremely active research area.

The first part of the course surveys the basic theory of optimal transport. Topics covered include: formulation of the problem, Kantorovich duality theory, existence and uniqueness theory, c-monotonicity and structure of solutions, discrete optimal transport. This is the core part of the course, which is important for all areas of application, and will be largely the same each year, although the presentation of some topics may vary slightly from year to year, to ensure compatibility with ‘X’.

The second part of the course develops applications in a particular area (corresponding to ‘X’ in the title), which rotates from year to year. In Fall, 2020, we will take ‘X’ = ”economics.” A surprisingly wide variety of problems in economic theory, econometrics and operations research are naturally formulated in terms of optimal transport. As a simple, illustrative example, consider an employer assigning a large number of heterogeneous employees to a diverse set of tasks. The employees have different skill sets which affect their proficiency at different jobs in different ways; matching a particular worker with a particular job results in a surplus which depends on the characteristics of both the worker and job. Assigning the workers to tasks to maximize the overall surplus is an optimal transport problem.

Many other examples arise in econometrics (where optimal transport can be used to optimize the estimation of incomplete information, or where multi-variate generalizations of quantiles, constructed using optimal transport, can be used to study dependence structures between distributions), matching problems (matching spouses on the marriage market, or employees and employers on the labour market, for instance) industrial organization (screening problems), contract theory (hedonic or discrete choice models), and financial engineering (estimating model free bounds on derivative prices and optimizing portfolios).

In both parts, we aim to keep the presentation accessible to non-experts, so that students with no prior background in either optimal transport or economics can follow the course.

Senior undergraduates, master’s and PhD students in quantitative disciplines, such as pure and applied mathematics, statistics, computer science, economics and engineering. The course potentially may also be attractive to those working in industry with a strong background in one of these areas.

This iteration of the course will be taught by Brendan Pass, and enhanced by guest lectures from experts in applications of optimal transport in economics and finance.

In order to register for a PIMS digital course for the Western Dean’s agreement you must obtain the approval of the course instructor.

**Data Driven Models in Finance**- Ruppa Thulasiram: tulsi@cs.umanitoba.ca

**Graph Theory**- Karen Meagher: meagherk@uregina.ca

**Mathematical Modeling of Complex Fluids**- James Feng: james.feng@ubc.ca

**Optimal Transport + X**- Brendan Pass: pass@ualberta.ca

Once you have obtained their approval please complete the Western Dean’s agreement form . These forms need to be signed by your home institution department and school of graduate studies. The form then needs to be sent to the host institution for the course.

For help with completing the Western Dean’s agreement form, please contact the graduate student program coordinator at your institution. For more information about the agreement, please see the Western Dean’s Agreement website .