Thank you to everyone involved in the 2024 edition of Math to Power Industry. The 2025 edition will take place May 5 - 23, 2025. If you are interested in participating, either as a mentor or a student, please contact us or join the M2PI Mailing List for the latest information.

MathIndustry 2024

June 4-24, 2024

In June 2024 PIMS is holding a hybrid workshop called Math to Power Industry(M2PI 2024). This popular workshop pairs industry problems with mathematical talent and aims to benefit both the trainees participating in the program and the industrial partners. To learn more about how M2PI works, please read on, or see some of the past editions.



This project aims to use feature engineering to help track and evaluate parliamentary and legislative members. We will leverage public data and attempt to engineer new features which may enhance engagement and/or predictive and decision making analytics of legislative bodies.

Aerium Analytics
Aerium Analytics

The ability to detect man-made objects in vegetation would aid many fields of industry including – but not limited to – search and rescue as well as agriculture. The ability to locate man-made objects such as damaged modes of transportation, camping or hiking equipment, parachutes, etc. in said vegetation aids the chances of locating missing individuals involved in these cases. While much imagery of these objects exists, imagery of them in varying degrees of repair within vegetation is limited at best making standard machine learning detection methods difficult. In such cases, focusing on the vegetation may enable the detection of such objects through machine learning methods where imagery is limited. In agriculture, farming equipment may require repair while in the field where it is easily possible to misplace equipment in vegetation. This lost equipment could damage other farming equipment while work is being done. Locating lost equipment will help limit further damage of farming equipment saving not only money, but time and production as well.

For this project, the team will be given access to aerial multispectral imagery containing a variety of man-made objects located in vegetation. The goal of this project is to develop a method which can detect these random man-made objects using machine learning and computer vision techniques while investigating the benefits of multispectral data to solving this problem. The machine learning field of focus for this problem is that of anomaly detection.


At Awesense we’ve been building a platform for digital energy, with the goal of allowing easy access to and use of electrical grid data in order to build a myriad of applications and use cases for the decarbonized grid of the future, which will need to include more and more distributed energy resources (DERs) such as rooftop solar, batteries as well as electric vehicles (EVs).

Awesense has built a sandbox environment populated with synthetic but realistic data and exposing APIs on top of which such applications can be built. As such, what we are looking for is to create a collection of prototype applications demonstrating the power of the platform. Given the synthetic nature of the dataset we can make available, this would be more of a “deliver a method (and implementation of it)” type project than a “deliver insights” type project.

This involves coding some analyses and visualizations on top of said data and APIs. It would require good data wrangling + statistics + data visualization skills to design and then implement the best way to transform, aggregate and visualize the data for the use case at hand (see below). The data access APIs are in SQL form, so SQL querying skills would also be required. Beyond that, the tools and programming languages used to create the analyses and visualizations would be up to the students. Typical ones we have used include BI tools like Power BI or Tableau and notebooking applications like Jupyter or Zeppelin combined with programming languages like python or R.

If the participants don’t have any electrical background, we can teach enough of it to allow handling the given use case. For this year’s project, we have chosen a use case entitled “EV charger capacity study”. At a high level, this entails determining how many new EV chargers could be installed in a particular portion of the electrical distribution grid without overloading the capacity of the grid infrastructure at that location. This would allow distribution grid planners to determine whether or not to approve requests for “interconnection” of EV chargers; it would also allow them to plan for needed infrastructure upgrades to support more EV chargers in the future.

Environmental Instruments Canada
Environmental Instruments Canada

Residential radon progeny exposure is the second leading cause of lung cancer, after smoking. The two main radon isotopes are Rn-222, which is part of the uranium-238 decay chain, and Rn-220, also called thoron, which is part of the thorium-232 decay chain. There is currently much interest in the Rn-220 contribution to radon progeny exposure, which has so far been largely ignored. (Rn-220 has a relatively short half life and usually decays before it reaches the living areas in a house and it usually doesn’t show up in radon measurements. But, Rn-220 has a longer lived decay product which does reach living areas and contributes to radon progeny exposure. It can even exceed the Rn-222 contribution.)

Environmental Instruments Canada (EIC) produces a Radon Sniffer (see ), which is used by radon mitigators and building scientists to find radon entry points. These sniffers currently assume all radon is Rn-222. See the appendix for a more detailed description of how the sniffer works. We want to extend the functionality to Rn-220.

In a 2020 M2PI project, we came up with a dedicated sampling and counting sequence and developed the math to determine how much Rn-222 vs Rn-220 was in the air. This report is available to the team.

In this project, we wish to develop a method by which we can determine the presence of Rn-220 in the air, while the Radon Sniffer is continually sampling air and without having to run a dedicated thoron measurement sequence.


Principal Component Analysis (PCA), as well as Factor Analysis, are a couple of techniques used to increase data value by making data more interpretable while simultaneously preserving as much variability and information possible . Given large topic-indexed datasets reflecting activity by parliamentarians such as chamber interventions, committee interventions, bills, motions, and chamber votes how might such analytical techniques be used to reduce the dimensionality of these sets while increasing their interpretability? Can useful and efficient graphical displays for the public be generated through the application of such techniques to political data? What other types of data analysis methods may be used alongside such techniques to extract meaning from political data? What measures of similitude or difference between individual politicians or parties might be derived? How might such features help to measure political performance? How can topic indexes be aggregated to reflect similarities in political concern?


Long-distance dispersal of insects in fast moving air currents is increasingly recognized as an important driver of their dynamics at a landscape scale. Moreover, this type of dispersal has important implications for forest and agricultural crops impacted by insects. Because the detection and tracking of populations of flying insects remains challenging, it is rarely possible to determine where insects originated after they have dispersed long distances. Data from weather radars designed to detect precipitation may be useful tools for gaining insight into insect long distance dispersal because insect bodies and rain drops are often similar in size. Thus, within radar scans there is potential to quantify the density of insects departing to start long-distance dispersal as well as the movement trajectories of swarms–at least until they pass beyond the range of the radar. However, because Doppler weather radars are extremely sensitive and capable of detecting water vapor in clouds, it can be difficult to distinguish between potential insect signals and weather signals even when it is not ostensibly raining.

This project has two distinct objectives. First, we will investigate classification of radar images, based on motion, to identify insect swarms. Second, we will develop a mathematical model and numerical simulations to more easily distinguish distinguish meteorological and biotic signals.


Perfit is currently working on a virtual fitting room app that allows online shoppers to get a virtual preview of the fit of garments in their cart. The virtual fit is accomplished via simulations of cloth interacting with a customer avatar through particle collisions. Specifically, each piece of cloth is modeled using a mesh of interconnected particles. The company has made progress on collisions between cloth and avatar. However, there is a need for collisions between cloth and cloth that has remained a significant challenge for the company. For example, the capability for a virtual garment to interact with itself via cloth-on-cloth collision would allow improved wrinkle quality, and garments with pleats, such as a pleated skirt. The technical challenge facing the company is to enable the handling of cloth-on-cloth collisions in near real time. In a mathematical sense, an algorithm for cloth-on-cloth collisions would need to be developed that minimizes the number of operations required (e.g., avoids brute force particle searches) while maintaining sufficient accuracy.

Topics in geometry, physics, computational methods, and computer graphics are expected to arise while working on this problem. The preferred implementation of the solution is the Fortran programming language in order to facilitate integration with the existing physics engine. During the workshop, meshes for both customer avatar and cloth will be made available through GitHub for testing.

IOTO International

Performance metrics in sports have seen remarkable growth and development. What if we turned some of these mathematical tools on political performance? Building on last year’s M2PI project, the goal of this year’s project is to analyze data related to the activities of legislators in Canada and the USA with a view to developing engineered features which might reflect political performance. These engineered features should be granular enough to significantly inflect during the course of a parliamentary or legislative session, providing quantitative and comparative performance insight.

City of Winnipeg - Insect Control Branch

The City of Winnipeg’s Insect Control Branch (ICB) of the Public Works Department provides services to Winnipeg residents to control insects, including mosquitoes. The mosquito control program is based on an environmentally mindful insect control strategy, and includes: (1) A larviciding program that is 100% biological and uses four larviciding helicopters, (2) Monitoring and treating over 31,500 hectares of water area on an ongoing basis based on weather conditions, and (3) Monitoring for adult nuisance mosquitoes in New Jersey Light Traps beginning early May.

In this project, we will examine some of the key challenges facing the ICB such as (1) Predictive modelling of adult mosquito populations subject to changes in rainfall/soil moisture content and wind speed (2) Preditictive modelling of larval development subject to changes in spring and summer temperatures.

MathIndustry Editions

Explore the projects and reports from previous editions


MathIndustry 2023

In July 2023 PIMS is holding a hybrid workshop called Math to Power Industry(M2PI 2023). M2PI 2023 is green-themed! Workshop problems will involve clean energy, clean tech, problems related to climate change and other problems in the realm of climate resilience.

MathIndustry 2022

Math to Power Industry 2022 (M2PI 2022) will take place July 11-29, 2022. We are now inviting non-academic partner organizations to consider particpation in this workshop.

  • Organizations are invited to submit math challenges for teams of graduate students and postdoctorol fellows to tackle during the workshop
  • PIMS matches your company to an academic researcher who can provide support for developing the problem statement.
  • Companies provide an industry mentor to work closely with the team during the workshop for a minimum of two hours per day during the last 10 days of the workshop. Industry mentors are welcome to work more closely with the team if desired.
  • During the workshop our graduate student and postdoc participants will also receive professional and technical skills training relevant to STEM careers in industry.
  • Efforts will be made to link companies to talent during and beyond the workshop to explore internships and other hiring needs.

MathIndustry 2021

The final-report for m2pi 2021 is now available. It features projects from

  • Aerium Analytics
  • ATCO
  • CSTS Healthcare
  • City of Winnipeg - Insect Control Branch
  • IOTO International
  • McMillan-McGee
  • Natural Resources Canada
  • Serious Labs
  • TheoryMesh

MathIndustry 2020

Projects, reports, team members and other details are available on the MathIndustry 2020 page. .

  • Aerium Analytics Inc.
  • ATCO Ltd.
  • BC Financial Services Authority
  • Cenovus Energy Inc.
  • The Divi Project
  • Environmental Instruments Canada Inc.
  • Fotech Solutions
  • IOTO International Inc.
  • McMillan-McGee Corporation
  • Ovintiv Inc.


Training Bootcamps

Certified training programs

Tech Skills Training

Agile software development, virtual collaboration, open source toolchains

Business Skills Training

communication skills, project management, effective teams & ethics

Information for Students

M2PI is a full-time training and work-integrated learning opportunity for graduate students and post-doctoral fellows in the mathematical sciences. Undergraduate students are also eligible to apply. Successful applicants will be asked to confirm their availability for full-time participation during June 4-24, 2024

  • In the first two weeks, students will receive professional and technical skills training relevant to non-academic STEM careers through virtual courses.
  • Following training, teams continue their work virtually and have access to a mentor.
  • At the conclusion, there will be an in-person graduate event with a career fair in Calgary on June 25th. Students and postdoctoral fellows who have been admitted to the M2PI program will receive a travel grant to attend this event. M2PI Fellows will have the opportunity to meet employers, learn about careers involving mathematics, and learn about job opportunities.

Following the workshop, your work will be showcased at a virtual graduation event. Here you will showcase your skills to a large audience including potential employers in both academic and non-academic fields. You can see examples of successful projects on the 2023 Math to Power Industry page.

Applications for M2PI 2024 are now closed. We will be accepting applications for the 2025 edition at the beginning of 2025. If you are interested in participating in future editions as a student, please consider registering for the M2PI Mailing List

Join the M2PI mailing list

Information for Employers

During June 4-24, 2024 PIMS is holding a virtual workshop called Math to Power Industry. We are currently accepting problem statements from employers who would like to submit a project to the workshop. If you would like to submit a problem or showcase a job opportunity, please contact us by completing the contact form below.

How it works

  • Organizations are invited to submit challenges for teams of graduate students and postdoctoral fellows to tackle during the workshop.
  • PIMS matches your organization to an academic researcher who can provide support for developing the problem statement.
  • Students complete various training courses in a variety of professional and technical skills relevant to STEM careers.
  • Mentors from the organization work closely with the student team for the remainder of the workshop. Mentors typically meet with the students for a minimum of two hours each day during this time.
  • Efforts will be made to link organizations to talent during and beyond the workshop for the purpose of filling internship or permanent positions.

The intended outcome is that partner organizations will have the opportunity to engage with highly skilled talent, while also receiving innovative solutions to the math challenge submitted to the workshop. Mentors from your organization are encouraged to meet student participants face-to-face during a graduation event on June 25, 2024 in Calgary, AB. There will also be an opportunity for all partner organizations to present job openings, internships, or other opportunities to students at this event.

Participate as an M2PI Mentor


LinkedIn Life

This session will look at how to make effective use of linkedin, helping you build your networks and shape your career.

Python Training in Numpy & Pandas

Participants will be trained in the use of numpy and pandas. This course will serve as a foundation for further courses on machine learning (e.g. sklearn) which will in turn provide participants with a solid general purpose toolset for data-analysis and developing data-science pipelines for practical problems.

The Opportunity

MathIndustry (Math to power industry) is a professional development school positioned to benefit Canadian industry because:

  1. Recent PhDs and Postdocs in the mathematical sciences are a national resource that is poised to be underutilized.
  2. Ideas from the mathematical sciences are vital to Canada’s industry sectors and are especially important during and after the pandemic.
  3. A cohort-based training and job placement program focused on key industry sectors will help advance Canada’s economy.

The drastic decrease in economic activity caused by the pandemic combined with cost explosions in other governmental programs will lead to significant cuts in higher education budgets. Reductions in the capacity of universities to hire new faculty and postdocs will essentially eliminate a career pathway for a generation of young researchers. This talent pool should be effectively redirected toward activities that drive Canada’s economic recovery.

The important role that mathematical scientists play in defining government policy responses to the pandemic is analogous to the role these experts should play across Canada’s industry sectors. Governmental decisions regarding when or how to optimally implement policies to flatten the curve rely upon predictive models, data analysis, and other mathematical insights. Effective business decision-making similarly requires expertise in modeling, computation, statistics, optimization (mathematical sciences). Studies by Deloitte have revealed the enormous impact the mathematical sciences have on the UK Economy and the Dutch Economy. Goals for the MathIndustry include economic stimulation during and after the COVID-19 pandemic, placement of recent mathematical science PhDs into jobs in western Canada, and an ongoing improvement to Canada’s Business Enterprise Research and Development capacity.

The Plan

The Pacific Institute for the Mathematical Sciences (PIMS) and partners are offering a virtual rapid response program to train and place young mathematical scientists into jobs in important industry sectors in western Canada (agrifood, energy, forestry, health care, mining). This program will start with a training bootcamp (software best practices, business, communications, project management), group collaborations with industry partners, and create a funnel leading to job placements in industry.


Contact Us

If you'd like to stay up to date with M2PI, please consider joining the m2pi mailing list. The list is used for announcements related to M2PI as well as opportunities relevant to participants.

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If instead you have a question or are interested in participating as an industry mentor or student participant, please complete this form.