MathIndustry 2023

July 10-28, 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.

Meet the Teams

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Aaron Slobodin

North Coast Skeena First Nation Stewardship Society Project Member

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Aashish Goyal

Innovatree Project Member

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Ahmed Adel Mahmoud

Cenovus Project Member

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Benjamin Bruce

IOTO Project Member

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C Shijia Yu

Multiverse Project Member

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Cameron Davies

Awesense Project Member

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Christopher van Bommel

Cenovus Project Member

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Dev Gokal

Cenovus Project Member

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Dulari Kulathunga

Cenovus Project Member

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Joel Benesh

Innovatree Project Member

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Kimathra Reddy

Multiverse Project Member

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Kitt Powers

Innovatree Project Member

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Manish Krishan Lal

Multiverse Project Member

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Meredith Sargent

Awesense Project Member

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Mishty Ray

Awesense Project Member

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Mohamar Rios Flores

Awesense Project Member

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Natalia Accomazzo Scotti

IOTO Project Member

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Patrice Moisan-Roy

Awesense Project Member

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Prajeet Bajpai

North Coast Skeena First Nations Stewardship Society Project Member

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Reza Sadoughian

Multiverse Project Member

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Riuke Xu

North Coast Skeena First Nation Stewardship Society Project Member

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Saeyon Mylvaganam

North Coast Skeena First Nation Stewardship Society Project Member

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Santanil Jana

Multiverse Project Member

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Solaleh Bolvardizadeh

Innovatree Project Member

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Wuqian Effie Gao

IOTO Project Member

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Yujia Yin

IOTO Project Member

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John Sang Jin Kang

Academic Mentor

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Leonard Olien

Academic Mentor

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Li Xing

Academic Mentor

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Mark Lewis

Academic Mentor

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Martin Krkosek

Academic Mentor

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Patrick Walls

Academic Mentor

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Raymond Spiteri

Academic Mentor

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Alex Razoumov

Parallel Coding with Julia Instructor

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Charlotte Anyango Ong'ang'a

Scientific Communication Instructor

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Ian Allison

Numpy & Pandas (Python) Instructor

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Kirby James

Leadership Skills Instructor

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Leonard Olien

Mathematical Modelling, Carbon Pricing Instructor

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Lorena Solis

Ethics, Diversity and Inclusion Instructor

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Marie-Hélène Burle

Git and GitHub instructor, SKLearn Instructor

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Samantha Jones

Equity, Diversity and Inclusion Instructor

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

Open Call: Organizing Committee for M2PI2024

We are excited to announce an open call for individuals interested in joining the Organizing Committee for Math to Power Industry 2024 (M2PI2024). We are seeking enthusiastic and dedicated members of the PIMS Network to join the dedicated team which has made this event a success since 2020.

What is Math to Power Industry?

Math to Power Industry is an annual event organized by PIMS with the primary objective of connecting graduate students with mentorship opportunities provided by industry professionals. This is achieved through hands-on collaborative projects that enable students to apply their mathematical knowledge to real-world industry challenges. The event serves as a bridge between academia and industry, fostering meaningful connections that can propel the careers of emerging mathematicians and contribute to industry advancements.

Leadership for Event Planning

The planning for M2PI2024 will be led by Kristine Bauer, Chair of the Organizing Committee, and Carrie Ragan, Program Administrator. Together with past members of the organizing committee, they have produced an event organizing package which will provide the framework for the 2024 event. We are seeking input and expertise from new organizing committee members to implement this framework and continue to evolve this successful program. Past M2PI Organizing Committee members include Allen Herman (Chair of the 2022 Committee), Anthony Quas, Ruth Situma and Ian Allison.

Why Join the Organizing Committee?

By becoming a part of the M2PI2024 Organizing Committee, you will have the opportunity to contribute to the success of this unique initiative. Committee members will gain substantial experience in helping graduate students in the mathematical sciences connect to non-academic career pathways. Committee members will play a crucial role in:

  • Mentorship and Industry Relations: Work to secure industry professionals who will serve as mentors and provide valuable guidance to the participating graduate students. Collaborate on the creation of hands-on projects that will be undertaken by graduate students during the event.

  • Event Logistics and Promotion: With the support of Kristine and Carrie and the PIMS team, assist with the logistical aspects of the event including course selection, recruitment, and review of application files to ensure that the event benefits the participants as much as possible.

  • Participant Engagement: Facilitate networking opportunities and workshops that enhance the interaction between graduate students and industry mentors.

How to Get Involved

If you are passionate about fostering connections between the emerging generation of mathematicians and industry professionals, eager to contribute your expertise, and excited about promoting the value of mathematics in solving real-world problems, we encourage you to apply to join the Organizing Committee. To express your interest, please email us at industry@pims.math.ca by Monday, November 20 with your name, a brief statement of your background and interests, and how you would like to contribute to the Math to Power Industry 2024 event.

This is your chance to make a substantial impact on the mathematical community by facilitating invaluable mentorship and hands-on experience for graduate students. We look forward to welcoming new members to our Organizing Committee and working together to make M2PI2024 a resounding success.

For any questions or further information, please do not hesitate to reach out to Kristine Bauer industry@pims.math.ca.

Thank you for your commitment to the advancement of mathematics and the empowerment of the next generation of mathematicians in the industrial landscape.

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 July 10 - 28, 2023.

  • During July 10-14, students will receive professional and technical skills training relevant to non-academic STEM careers through virtual courses.
  • During July 20-28, teams continue their work virtually and have access to a mentor.
  • There will be a virtual career fair where you will have the opportunity to meet employers, learn about careers involving mathematics, and learn about internship or job openings.

Following the workshop, your work will be showcased at a virtual graduation event on July 31. 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 2022 Math to Power Industry page.

Apply now

Information for Employers

During July 10 - 31 2023 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 Kristine Bauer (industry@pims.math.ca) or use the contact form below.

How it works

  • Organizations are invited to submit green-themed math 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.
  • Organizations provide mentors to work closely with the team during July 17-27. During July 17-19, teams will meet in person either on site or at a local PIMS host university. During July 20-27, teams work virtually and need to have access to a mentor from your organization for minimum of two hours per day. 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 non-academic STEM careers.
  • Efforts will be made to link organizations to talent during and beyond the workshop for the purpose of filling internship or permanent positions.

We suggest that organizations provide a $1000 stipend for each of the four students on their team, similar to an intern salary. This will help to recruit exceptional teams of students. This is not a requirement for participating in M2PI, and there is no other cost for employer partners to participate in the program.

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. You can see examples of successful projects on the 2022 M2PI website.

Contact Us

Courses

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.

LinkedIn Life

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

Projects

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IOTO

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.

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.

Schedule

The schedule below is subject to change, please consult the m2pi program document for updates. All times are listed in MDT.

Week 1: Team Orientation, Tech and Business

TimeMondayTuesdayWednesdayThursdayFriday
9:00 - 12:00M2PI 2023 Welcome
(Kristine Bauer, UCalgary)
(Carrie Ragan, UCalgary)
Python: Numpy, Pandas
(Ian Allison, PIMS)
Git/GitHub
(Marie-Helene Burle, SFU)
(Alex Razoumov , SFU)
SKLearn
(Marie-Helene Burle, SFU)
Session 1: Parallel Coding with Julia
(Alex Razoumov, SFU)
Session 2: Problem session
(Ian Allison, PIMS)
(Marie-Helene Burle, SFU)
12:00 - 13:00Break
13:00 - 14:30Equity, Diversity and Inclusion
(Lorena Solis and Samantha Jones, UCalgary)
LinkedIn and Resume Writing
(Charlotte Ong'ang'a, UCalgary)
Mathematical modelling, Carbon Pricing
(Leonard Olien, UCalgary)
Leadership Skills
(Kirby James, Mitacs)
Info Session for teams
14:30 - 16:30Meet the mentors/problem overviews
(Kristine Bauer, UCalgary)

Week 2: Interfacing with the world outside of academia

TimeMondayTuesdayWednesdayThursdayFriday
9:00 - 12:00Team MeetingsTeam MeetingsTeam MeetingsTeam MeetingsTeam Meetings
12:00 - 13:00Break
13:00 - 14:00Job fair session
Multiverse Computing
(Mehdi Bozzo-Rey)
Titte Institute for Mathematics and Computing
(Megan Dewar)
Job fair session
IOTO International
(William Spat)
ATCO SpaceLab
(Nisha Mohan, Justin Young)
Job fair session
Big River Analytics
(Hannes Edinger)
Job fair session
PINNs Microcredentials
(Vajhtang Poutkaradze)
Job fair session
Sustainable Energy Development UofC
Sara Hastings-Simon
14:00 - 16:00Team MeetingsTeam MeetingsTeam MeetingsTeam MeetingsTeam Meetings

Week 3: Collaboration on industrial problems and final presentations

TimeMondayTuesdayWednesdayThursdayFriday
9:00 - 12:0010-Min check in:
Update from Team 1 and Team 2
+
Team Meetings
10-Min check in:
Update from Team 3 and Team 4
+
Team Meetings
10-Min check in:
Update from Team 5
+
Updates from PIMS
+
Team Meetings
10-Min check in:
Update from Team 6
+
Updates from PIMS
+
Team Meetings
Updates from PIMS
+
Team Meetings
12:00 - 13:00Break
13:00 - 13:30Team MeetingsTeam MeetingsTeam MeetingsTeam Meetings2i2c
(Jim Colliander)
13:30 - 16:30M2PI Final Gala Practice and Exit Surveys

Final Gala/graduation event: Monday July 31, 2023 15:00-17:00 (MDT), register here to participate.

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