MathIndustry 2021

August 3-27, 2021

Together with our partners, PIMS is excited to offer a 4-week virtual MathIndustry workshop for graduate students and postdoctoral fellows in the mathematical and statistical sciences to gain the industry skills needed for success in their careers.

This workshop has two main objectives:

  1. To build technical skills in programming and computational workflows
  2. To build business skills for effective teamwork and technical report writing

Attendees will gain hands-on experience as part of a team working on a real-world problem posed by an industry partner. Potential industry partners can also contact us to learn more or propose projects.

The Gala and final presentations for the M2PI 2021 projects will take place on August 26th at 12/1 - 3/4 (Pacific/Mountain). Please contact us for connection details to join us for this event.

Meet the Teams

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Adeyemi Fagbade

Team 2 - ATCO

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Aidin Zaherparandaz

Team 4 - City of Winnipeg ICB

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Alexandra McSween

Team 2 - ATCO

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Amit jha

Team 1 - Aerium Analytics

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Aniket Joshi

Team 5 - IOTO

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Arian Khorasani

Team 6 - McMillan McGee

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Arnaud Ngopnang

Team 9 - Serious Labs

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Avleen Kaur

Team 1 - Aerium Analytics

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Bahar Mousazadeh

Team 9 - Serious Labs

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Bryan Kettle

Team 7 - NRCAN 1

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Carson Chambers

Team 6 - McMillian McGee

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Edgar Pacheco Castan

Team 10 - TheoryMesh

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Ellie Thieu

Team 3 - CSTS Healthcare

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Eric Rozon

Team 3 - CSTS Healthcare

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Guojun Ma

Team 5 - IOTO

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Hamid Hamidi

Team 1 - Aerium Analytics

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Igor Pinheiro

Team 10 - TheoryMesh

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Joel Benesh

Team 8 - NRCAN 2

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Jules Hoepner

Team 8 - NRCAN 2

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Mahsa N. Shirazi

Team 2 - ATCO

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Maksym Neyra-Nesterenko

Team 9 - Serious Labs

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Mishty Ray

Team 8 - NRCAN 2

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Mitch Haslehurst

Team 5 - IOTO International

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Moumita Shau

Team 9 - Serious Labs

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Natalia Accomazzo Scotti

Team 3 - CSTS Healthcare

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Nimanthi Yaseema

Team 2 - ATCO

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Noah Bolohan

Team 7 - NRCAN 1

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Parham Hamidi

Team 1 - Aerium Analytics

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Pedro Jose Sobrevilla Moreno

Team 6 - McMillan-McGee

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Rachel Han

Team 8 - NRCAN 2

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Saimon Yeal Islam

Team 10 - TheoryMesh

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Sam Simon

Team 5 - IOTO

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Shen-Ning Tung

Team 5 - IOTO

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Thomas Pender

Team 10 - TheoryMesh

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Xiaowei Li

Team 1 - Aerium Analytics

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Adam Kashlak

Academic Mentor

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Andrii Arman

Academic Mentor

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Aysa Fakheri Tabrizi

Academic Mentor

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Hui Huang

Academic Mentor

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Jonathan Gallagher

Academic Mentor

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Joshua Brinkerhoff

Academic Mentor

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Julien Arino

Academic Mentor

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Peijun Sang

Academic Mentor

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Shaun Lui

Academic Mentor

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Slim Ibrahim

Academic Mentor

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Matthew Greenberg

Numpy & Pandas (Python) Instructor

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Alex Razoumov

Parallel Coding with Julia 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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Laura Gutierrez Funderburk

Ethics in AI Instructor

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Lorena Solis

Ethics, Diversity and Inclusion Instructor

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Marie-Hélène Burle

Git and GitHub instructor

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Rob Baecker

Entrepreneurship Instructor

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Samantha Jones

Ethics Diversity and Inclusion Instructor

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Tom O'Neill

Effective Teams Instructor

MathIndustry Editions

Explore the projects and reports from previous editions of MathIndustry

 
 
 
 
 

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

Projects

.js-id-2021
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.

Partners