MathIndustry 2020

An Economic Stimulus Opportunity for Canada

2020 Projects

Explore projects and reports from the 2020 edition of M2PI

MathIndustry Editions

Explore the projects and reports from previous editions

 
 
 
 
 

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.

Skills

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

Projects

.js-id-2020
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.

Awesense
Awesense

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 https://radonsniffer.com/ ), 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.

IOTO

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?

NRCAN

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

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.

Meet the Teams

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Aaron(Xiang) Zheng

Project Ioto-international Member.

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Abhishek Kumar Shukla

Team Divi member

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Adili Masanika

ATCO Project member

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Alireza Yazdani

McMillan-McGee Team Member

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Amelia Spivak

ATCO Project member

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Ana Karen Roldan Contreras

Ovintiv Project member

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Anton Iatcenko

McMillan-McGee Team Member

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Benjamin MacAdam

McMillan-McGee Project Member

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Boya Peng

Fotech Solutions Project Member

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Brian Chan

Fotech Solutions project member

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Carlos Contreras

Aerium Analytics Project Member

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Chantelle Hanratty

ATCO Project Member

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Dana Berman

IOTO Goverlytics Project member

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Daniel Di Benedetto

BCFSA Project Member

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Danyi Liu

IOTO Goverlytic Project Member

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Diaaeldin Taha

IOTO Goverlytics Project Member

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Dongying Wang

BSFSA Project member

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Elham Soufiani

Project ovintiv member

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Emily Rose Korfanty

Fotech Solutions Project Member

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Erik Chan

Cenovus Project Member

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Evan MacNeil

Divi Project Member

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Jaeun Park

Fotech Solutions Project Member

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Jianou Zhang

Fotech Solutions Project member

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Jillian Glassett

IOTO Goverlytics project member

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Junjie Zhu

Aerium Analytics Project Member

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Keran Li

Aerium Project member

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Leimin Gao

BCFSA Project member member

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Li Wang

Aerium-Analytics Project Member

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Mahsa Azizi

Cenovus Project Memeber

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Mohsen Seifi

Ovintiv Project member

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Neha Sharma

BSFSA Project member

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Nishant Agrawal

Ovintiv Project member

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Noah Bolohan

Team 7 - NRCAN 1

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Ryan Thiessen

McMillan-McGee Team Member

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S.Parisa Torabi

Cenovus Energy Project Member

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Sarah Nataj

Project member, Fotech Solutions

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Shang Li

Divi Project Member

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Stephen Styles

Environmental Instruments Canada project member

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Wali Mohammad Abdullah

ATCO Project member

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Yanhong Xu

ATCO Project member

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Yao Yao

Ovintiv Project member

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Yi Sui

Aerium Analytics Project Member

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Yiwei Huang

BCFSA Project member

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Yu-Hsiang Liu

ATCO Project member

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Ananya Chattoraj

Ethics Instructor

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Diane Fletcher

Foundations in Project Management Instructor

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France Cloutier

Foundations in Project Management Instructor

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Ian Allison

Numpy & Pandas (Python) Instructor

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Jolen Galaugher

How to pitch a Solution Instructor

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Leonard Olien

Linked-in and Netlify Course Instructor

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Lynne Lamarche

Foundations in Project Management Instructor

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Mary Baetz

Foundations in Project Management Instructor