This workshop is intended to help students learn how to define equity, diversity and inclusion, engage in meaningful dialogs about challenges in these areas and explore solutions for creating more diverse and inclusive spaces.

This workshop will focus on building participant’s communication and other “soft skills”. These skill are critical to working in a research setting.

Mitacs will offer their Leadership Skills course to our workshop participants. You will need to enroll for this course on the EDGE portal and complete the (approx. 90 minute) asynchronous portion of the course prior to the workshop. On completion of the asynchronous part of the course and signing up for the facilitated session you will receive a Zoom link from Mitacs for the facilitated part of the course taking place on July 11th, 2022, from 1pm until 3:30 pm MDT.

Participants will explore some of the pitfalls of artificial intelligence when it is used to make decisions which will impacting people and also how to design ethical models.

Participants will receive instruction on using git and GitHub for version control software development and collaboration.

The aim of this project is to create an algorithm which identifies the areas of the electrical grid which are most unbalanced (across electrical phases) in order to identify places where load can be easily switched from one electrical phase to another in order to improve and optimize balance.

The goal of the Math to Power Industry Problem is to develop an algorithm that will determine the construction requirements per Sub-section 3.2.2 of the BC Building Code

This project aims to improve machine learning models for image classification of areas affected by deforestation. It will involve techniques such as feature engineering, binary classifier design and the use of evaulation metrics to design models which maximize precision while minimizing false positives.

This project will use natural language processing to generate search engine prompts for high-quality results for incidents at sea. Quality indicators include relevance, diversity, soundness, and minimizing bias.

This project aims to develop a strategy for determining performance indicators to compare different search engines for yielding high-quality results from web scraping of incidents occuring at sea.

This project aims to develop a strategy for leveraging large language models (LLMs) to identify relevant search results for incidents occuring at sea from web-scraped content, and to extract key information.

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).

Cenovus is a Canadian-based integrated energy company headquartered in Calgary. We’re committed to maximizing value by sustainably developing our assets in a safe, innovative and cost-efficient manner, integrating environmental, social and governance considerations into our business plans.

Model the carbon footprint of slash pile burning vs grinding for bioenergy in the forestry industry and create a web interface (using HTML) which can report those costs in real time

This project uses machine learning techniques to analyze data regarding deforestation in the Amazon basin. In a recent blog post, Ajitesh Kumar wrote: Forest degradation is a major problem that contributes to climate change.

This project aims to determine an appropriate tissue sample size program to effectively expand Genetic Stock Identification (GSI) data which indicates proportionate stock composition. Such a program would then be applied to the estimated harvest to produce meaningful stock proportion estimates in the mixed stock recreational Chinook harvest for Pacific Fishery Management Areas managed by the North Skeena First Nations Stewardship Society.

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.

Local labour market information (LMI), including employment, unemployment, and participation rates, are important for planning activities conducted by municipal and First Nation governments, service providers, and many more organizations. Census of Population (Census) data, published by Statistics Canada (StatCan), provides LMI at the Census Division (CD) and Census Subdivision (CSD) levels. However, the Census is only conducted every five years. This project asks participants to build a model which estimates intercensal LMI at the CD and CSD levels.

LMI are not publicly available at the CD and CSD levels of geography for years when the Census is not conducted. StatCan’s Labour Force Survey provides data on a monthly basis. However, StatCan does not publish Labour Force Survey data at the CD or CSD levels. As such, LMI at the Economic Region level are often used in the place of intercensal data on CDs or CSDs. Figure 1 shows the CSD of the District of Kitimat, as well as the larger CD and Economic Region it is within, as an example illustrating how Economic Regions may not serve as good proxies for CSDs.

Cedar Academy Society is building a digital representation or “digital twin” of a Lower Mainland city, powered by Unity3D engine. During the summer of 2022, we aim to complete the integration of real-time traffic data into such model to better reflect the dynamic nature of the communities we live in. Through previous studies, we have been able to associate traffic camera-captured images to object counts. This year, we intend to drive further and to project those object counts into visualized traffic in a virtual world.

Background

The stormwater infrastructure in many cities is facing challenges as the climate changes, rainfall patterns change and sea levels rise. To meet these challenges, cities are installing green infrastructure (GI) systems to absorb and retain rainfall where it lands and reduce sewer overflow. Across North America alone, cities are investing over $56.2 billion dollars in green infrastructure. By doing so, communities are becoming more resilient against climate change and are achieving environmental, social and economic benefits. Novion’s Climate Intelligence Platform helps cities monitor their green infrastructure for performance optimization, regulatory compliance, and maintenance.

Problem Statement

Once green infrastructure has been constructed, monitoring the changes in water level after a rainfall event is required to verify its performance and net impact. This project will investigate aspects of this monitoring.

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.

Cancer is a disease that affects 14M people each year. While we have had some success with specific cancers, many patients are put on a roller coaster of emotion through cycles of remission and relapse. There is abundant scientific evidence which tells us cancer is driven by multiple genes working in concert. CSTS Healthcare has developed a computational system which identifies personalized cancer therapy for every cancer patient given their unique set of DNA and RNA. In practice, the therapy a patient actually receives may not exactly match what our system has identified as the best therapy. In this project we will develop therapy similarity measures to allow us to compare our therapies with those actually given by to a patient by their oncologists.

Realtime simulators in virtual reality are a leading-edge technology to provide standardized training and evaluation that contribute to heavy-equipment operator safety. They aim to reduce accidents due to operator inexperience by training an operator in scenarios before they are expected to perform these same tasks in reality. To satisfy the high frame-rate requirements of real-time simulation, algorithms to solve these models must be extremely efficient and yield predictable and reproducible results.

The simulation of heavy equipment involves simulating both physical bodies in motion and hydraulic pressures. The goal of this project is to develop an approach to the simulation of fluid pressure volumes that can be interfaced with a position-based dynamics (PBD) simulation, improving performance and providing a more realistic experience.

Among the renewable and clean energy technologies, wind energy is one of the most efficient, costing 1–2 cents per kilowatt-hour after the production tax credit by governments. While natural wind speeds over various continents in the world span from 0 to 20 m/s, Vertical Axis Wind Turbines (VAWT) placed on highway medians make it possible to utilize consistently higher wind speeds due to vehicle motion. Additionally, the energy generated by these wind turbines is reported to increase multi-fold due to the shearing winds generated on both sides of the medians by the on-going traffic. In this project, we will seek to optimize the positioning of turbines to achieve optimal results using criteria determined to be important, such as output power, ease of installation/repair, proximity to consumers, additive effects from positioning etc. We will use real-life traffic, geographical and weather data, and subsequently investigate economic feasibility of implementation of this technology.

McMillan-McGee have developed induction heating technology for in-situ soil remediation. As such, we have developed heater casings, work coils, and inverters which generate high frequency alternating current. We have also developed a great amount of the analytical work for engineering this equipment.

An important aspect of our inverter design (or for that matter any high frequency inverter in general) is to have a good understanding of the electrical properties (resistance and inductive reactance) of the DC bus bar that supplies current to the high speed switching devices (IGBTs and SiC Mosfets). Knowing this would allow us to design a suitable bus bar system that can absorb energy caused by switching transients from these semiconductor devices as a result of commuting current through the work coil. The problems in this project centre on solving certain boundary value problems involving Maxwell’s equations and linking these to the laws of thermodynamics.

Insect dispersal is often divided into two classes: local and long distance. Local dispersal is the most common dispersal mode and because many individuals disperse this way, it is well described by dispersal kernels. Long distance dispersal is more stochastic and difficult to model using dispersal kernels because only a small proportion of insects are thought to disperse long distances. For the mountain pine beetle for example, most individuals disperse between five and fifty meters from where they were born but about 0.2 percent of individuals end up above tree canopies where they can be pulled upwards by updrafts and then transported laterally by higher wind speeds in the lower atmosphere.

Long-distance dispersal is likely the dominant determinant of the speed of mountain pine beetle invasions, but estimation of invasion speeds using standard mathematical approaches are impeded by the strong Allee effect. In this project, we will explore theoretical dispersion distributions and models to better estimate mountain pine beetle invasion speed.

Existing techniques for insect mark and recapture studies can result in unnatural dispersal patterns. We have developed a new technique for marking insects as the emerge from which offers a much more natural dispersal pattern.

The technique involves coating trees with a special paper which glows under ultra-violet light. The insects are marked with paper dust as they chew through the paper to emerge. Following recapture, they can be examined for traces of the paper under ultra-violet light.

In this project we will work with images of insects marked using this technique and control images of unmarked insects. The problem is to conduct batch analysis of the images and classify them as marked and unmarked to validate the new technique.

Producing food and other goods sustainability is the greatest challenge of our generation. To produce food sustainably there are many factors to consider including the types of crops, the inputs, organic vs. non-organic, use of fertilizer, food waste, transportation to market and other factors. The goal of this project is to develop a model which can take into account factors end-to-end in the food supply chain to inform decisions on methods (organic, traditional, vertical, etc.) and sustainability indicators (green house gases, etc.).

Roads are a vital component of today’s society. They not only connect us to different cities, towns, provinces, and countries, but they also provide of a means of transporting goods and services. Typically, roads are constructed using materials such as concrete, or asphalt. Between the cold winter temperatures and hot summer temperatures, as well as settling earth, cracks and potholes are bound to occur on roads.

Alberta spent over 1.6 billion dollars in transportation related needs in 2020-21. Over 26% of this money went towards road construction and maintenance related expenditures. Roads undergo regular inspection; a detailed Surface Condition Rating occurs every two years in Alberta. This helps determine the priority in which maintenance should occur on roads in the province. One method of conducting these inspections could be done via aerial imagery. While an individual could go through these images to inspect the roads for cracks and potholes, is there a way that machine learning and computer vision could not only detect cracks but also classify their severity?

This project concerns the transportation of heavy oil via pipeline, and the impact of congestion in transportation on pricing. Using stochastic transport optimization can we model and answer the following questions: When there are documented disruptions in the transport system, can we predict how large the congestion surcharge was and how prices responded to the disruption? Can we predict the occurrence of congestion by perturbing input factors in the system? How do shape and connections in the transport network contribute to the propensity for frequency of the congestion and magnitude of congestion surcharge?

Due to the outbreak of Covid-19 around the world, and government policies implemented as a response to the outbreak, many corporations have chosen to let their employees work from home to prevent the spread of the disease. In order to safely re-open the economy, one of the recommendations from health authorities is to allow only a limited percentage of workers in the workplace at any specific time. Given these constraints, it is useful for companies to arrange flexible work schedule so the employees go to offices during reasonable working hours, and at the same time reduce their commute time to improve their productivity. In this problem, you will help a model business to design and optimize their employees’ working-at-office schedule using a combination of criteria which you deem to be important, and real-life data such as traffic, limitation on work schedule hours, commuting time and others.

The main limitation of blockchains is storage requirements, which would be alleviated if one could reversibly compress the data in a blockchain or in its underlying transaction graph. Determine to what extent a transaction graph can be compressed (for later decompression) or what obstructions exist to its compression. What compression ratio can you achieve for an ordered sequence of cryptographic hashes? Pure Mathematics skills and experience with mathematical proof-writing are essential skills for this project. Knowledge of undergraduate-level cryptography or Python programming skills would be assets, but are not required.

The goal of this project is to develop a housing price estimate/forecast using publicly available data to inform evidence-based decision making for the benefit of government regulators, industry practitioners, and concerned citizens. Students will be expected to use Python 3.X for data acquisition, cleaning, organization, and manipulation. Working experience with libraries such as Pandas may be useful. Previous experience with other programming languages such as Matlab or R is useful but not required.

Environmental Instruments Canada (EIC) produces a Radon Sniffer which is used to find radon entry points. One method of determining the ratio of Radon 222 to Radon 220 (thoron) in the air is by implementing sampling and counting sequences and observing the change in the alpha count over time. The goal of this project is to develop an optimized sampling and counting sequence that results in the best statistical accuracy. Understanding radioactive decay and the coupled differential equations describing a decay series would be useful. A team with statistical expertise would be essential. Some familiarity with spreadsheets such as Excel would be helpful.

Consider a distributed acoustic sensing (DAS) system monitoring a fibre optic cable deployed along an active roadway. The goal of this project is to use data collected from the DAS system to develop a detection and tracking method capable of identifying individual vehicles and reporting their position and velocity as they move along the road/fibre. Once the position and velocity are determined, various metrics for traffic flow could be determined, allowing for prediction and optimization of traffic congestion.

Performance metrics in sports have seen remarkable growth and development. What if we turned some of these mathematical tools on political performance? The goal of this project is to analyze data which are related to the progression of a bill into law in the US. A background in statistics or graph theory would be helpful. Some background in computer programming or data science may be helpful, but not necessary.

The goal of this project is to develop a reasonably accurate and affordable design tool to model the performance of McMillan-McGee’s patented induction heaters, which are used for thermal conductive remediation of contaminated soil. A good design tool would be useful to assess the feasibility and cost of using different heater lengths, diameters, and materials. Working knowledge of Maxwell’s equations, vector analysis, boundary value problems, Green’s functions, complex variables, contour integration, residues, integral transformations and differential equations would be essential background for this project.

In March 2020, the WTI futures contract settled below zero for the first time in the contract’s history. Many market participants apply the Black 76 model or a variation of this model to calculate the value of the options on this futures contract. However, Black 76 requires positive underlying market prices. The goal of this project is to identify alternative models which can accept negative underlying pricing, and assess the suitability of the alternatives. People interested in quantitative finance, commodity training and marketing, and bridging the gap between quantitative experts and non-experts would be excellent team members for this project.

Standard procedure for building training sets for some machine learning models involves an individual going through hundreds of images and creating 2D binary matrices which reflects where the region of interest is in each image. Depending on the type of images, can we use RGB information or some other method to automate this process? The goal of this project is to develop a method which creates a mask of an image depicting where monochromatic objects occur in an image automatically and with limited user input.