Projets novateurs réalisés

Machine Learning developer intern working with the Government of Alberta to develop and commercialize AI-powered solutions in the Public Services sector (1)

The project comprises internships in a variety of technical and business roles within our organization and within our GovLab program. Within the organization, roles include associate machine learning developer, business development associate, communications associate and finance associate. Within GovLab specifically, roles include associate machine learning developer, associate business solutions consultant, and project delivery associate. The difference is mainly that in GovLab, there is a focus on public sector problems, whereas in AltaML overall, we work across sectors.

Volting : Une solution de mobilité augmentée

Au Canada, 288 800 personnes âgées de 15 ans et plus utilisent un fauteuil roulant manuel ou motorisé ou un quadriporteur. Les bénéfices de aides à la mobilité pour les personnes ayant des incapacités sont bien connus. Volting (www.volting.org) est une technologie actuellement en développement qui se distingue du fauteuil roulant par sa capacité à libérer les mains de l’utilisateur. Il n’y a plus de phase de poussée. En repensant la mécanique du fauteuil, Volting offre à l’utilisateur la possibilité d’incliner latéralement le fauteuil (troisième degré de liberté). Actuellement, Volting est exploré dans trois domaines clés : la danse, le sport adapté et la réadaptation. Ces applications ouvrent des perspectives passionnantes pour l’amélioration de la participation sociale et de l’autonomie des personnes ayant des incapacités motrices. Étant donné la nature innovante de Volting, une approche d’évaluation de ses bénéfices qui soit spécifique à ses caractéristiques est requise. Ainsi, l’objectif du projet consiste à codévelopper et à tester la faisabilité d’un protocole d’évaluation de Volting dans des contextes variés (arts/danse, sport adapté et activités de réadaptation). Cet objectif sera atteint par une démarche où les chercheurs et les utilisateurs potentiels de Volting seront consultés de différentes manières.

L2M – Enhancing Skin Care Efficacy and Stability with Development of Nanogel-Based Delivery Systems

Our technology surrounds the development of nanogel-based delivery systems to enhance the efficacy and stability of cosmetic products for skin care. Nanogels encapsulate active ingredients, protecting them from degradation and sustained release on the skin surface. This can act as a delivery system to ensure beneficial compounds’ controlled and sustained release, maximizing their impact. The nanogels are hygroscopic and can form a hydrating layer on the skin, improving moisture retention and providing a smooth, non-greasy feel, making them ideal for products like moisturizers and serums. The nanogels will be designed with environmentally friendly materials, reducing the ecological footprint of cosmetic formulations.

Use of Metakaolin as Alternative Supplementary Cementitious Materials in 3D Printing of Concrete

3D Concrete Printing (3DCP) could potentially increase the efficiency of construction projects and profitability. However, 3DCP typically mixes contain high volume of paste which implies high volume of binder. The expeditious development of 3DCP requires exploring other prospective and sustainable mixes besides the use of Ordinary Portland Cement (OPC) based mixes to meet the high-volume binder content requirement. The cement industry emits approximately 814-935 kg of carbon dioxide. As such, the most cost-effective and environmentally friendly way to meet the high binder dosage requirement in 3DCP is the use Supplementary Cementitious Materials (SCMs). To respond to the relative scarcity and diminishing of traditional SCMs, the proposed project aims at synthesizing and optimization of locally available calcined clay (metakaolin) as an alternative SCM for 3DCP purposes. Success of this research will help in accelerating the widespread adoption of metakaolin and the resulting type of blended cement across Canada and elsewhere.

Understanding sex-differences in heart valve calcification through correlative cryo-microscopy

CAVD shows in the deposition of calcifications in the aortic valve, which eventually leads to heart failure. We recently found that the types (phases) and morphologies of calcifications that form in the disease are completely different in men and women. This suggests that calcification in the two sexes follows an entirely different pathway. In this project, we want to better understand the different calcification pathways in men and women by relating the phases and the morphologies of the minerals in the two sexes to each other and to their associated tissue components. We will use a state-of-the-art correlative cryogenic Raman mapping and transmission electron microscopy (TEM) set-up at Radboud University Medical Center. Raman allows us to map the chemical composition of the heart valve tissue, while cryogenic TEM generates highly magnified images of the valve calcifications with corresponding phase information. The exchange between our spectroscopy expertise and our partner laboratory’s expertise in multi-scale microscopic analysis will allow both groups to more thoroughly answer mineralization-related questions. Applying these complementary techniques to heart valves from CAVD patients will elucidate the sex-specific calcification mechanisms of the disease, which could pave the way for the development of new sex-specific detection and treatment methods.

Circular Food Innovation Lab II

Wasted food – the result of a linear pattern of producing, under-consuming and disposing of food – is a pervasive problem globally and in Canada, where 58% of the food produced is wasted each year: that’s 11.17 million tonnes of edible food at a value of $49.46 billion (Varney, 2021). Wasted food is a complex challenge, in that it intersects with issues of food security, climate change, social justice and health, and involves many actors across the public and private sector. In Vancouver, the pandemic, extreme weather events, increased inflation and extractive relationships with lands and waters have disproportionately affected food access for systemically marginalized communities, resulting in increased reliance on charitable food sources (Soma, 2022; Varney, 2022). Meanwhile, many local food charities and non-profits are questioning the trajectory and effectiveness of the charitable food system in Canada in addressing food insecurity, and their role within it (Pellegrini, 2024).

This project will use action research and co-design methods, and focuses on deepening the relationships of food system stakeholders and rights-holders, reducing wasted food, and promoting an equitable and culturally meaningful food system. Ultimately, the pilot project will study the conditions for an equitable circular economy of food.

L2M – Reinforcement-Learning-Driven Electronic Design Automation (EDA) for Optimal Layout Placement

Reinforcement Learning (RL) driven Electronic Design Automation (EDA) is revolutionizing layout placement optimization for integrated circuits, enabling a faster design process. By incorporating the RL techniques, we enhance the historically precise yet labor-intensive process for smart integrated circuit (IC) fabrication. This innovative approach streamlines IC design, accounting for factors such as foggy and proximity effects, promoting both efficiency and accuracy in the layout placement design.

Optical diagnostics for probabilistic quantification of defects in functionalized 2D nanomaterials

This project aims to quantify defects in the probability framework for 2-dimensional (2D) materials with superior performance using optical techniques, primarily focusing on molybdenum disulfide (MoS2). Additionally, defects are an important factor influencing the performance of single-photon emitters (SPEs), a type of quantum device. This project will directly relate the SPE performance with defect concentration. Thus, the second part of the project involves fabricating SPEs based on Argon-ion irradiated/ strain-induced defective MoS2 and correlating device performance with a predeveloped statistical inference prediction model. This project will benefit participating institutions by advancing the development of 2D material applications in the quantum field and enhancing the understanding of material defects. This research project will help to build a foundation for large-scale production in the future.

Development of a robust, low-complexity infant cry classification system

This proposal aims to develop a robust, low complexity infant cry deep learning classification based on various babies’ responses to physiological needs such as hunger or to discomfort and pain. The significance of this research lies in its potential to enhance early detection of needs and moods in newborns, contributing to improved infant care, early intervention and augmented infant-parent communication.
The novelty of the proposed research lies in applying methods to improve performance when small datasets are available and to reduce complexity in deep learning classification systems for infant cries. In this project we will also select and benchmark multiple datasets for training and testing, evaluate and compare different methodologies for feature selection and scaling, and implement a model suitable for real-time applications.
Future research will be extended in subsequent years to include the detection of additional emotional responses, and include babies who are diagnosed with medical conditions, but the emphasis of this one-year research proposal is on machine learning, engineering, and computer science aspects, and it will be performed using existing public domain datasets.

L2M – Satellite Monitoring, Analysis, and Reporting Tool for Harmful Algae Bloom identification: introducing SMART-HAB, a machine-learning tool to identify and visualize harmful algae blooms in near-real time.

Harmful algal blooms (HABs) are a growing threat to drinking water, fisheries, public health, and recreation. In recent years, HABs have increased in frequency and severity in both freshwater and marine environments. Blooms are hard to monitor because they can occur unexpectedly, and reporting methods across Canada are inconsistent, creating a patchwork of alerting methods for industry and public sectors to rely on. Funding to monitor HAB activity is increasing, but current methods are expensive and time consuming. Field teams are limited by water quality testing capacity, and even buoys with remote sensors monitoring water quality can only test water that interacts with the device.

Our team is developing an addition to the HAB monitoring and alert network with the Satellite Monitoring and Reporting Tool for HAB identification (SMART-HAB). SMART-HAB uses satellite imagery with high temporal and spatial resolution to identify potential HABs in Canadian waters using machine learning algorithms. This application can greatly increase monitoring groups’ capabilities by showing users where HABs are occurring, saving both time and resources. SMART-HAB has experienced success in estimating bloom severity, and has been positively correlated to in-situ measurements of cyanobacteria blooms. We hope that SMART-HAB can be used across the country to create a consistent, widespread monitoring network, and alert users in near-real time of HAB activity, severity and extent. With the help of Mitacs and Springboard Atlantic Inc., we are refining our product for future users to prepare SMART-HAB for market.

L2M – Enabling knowledge transfer between science education and coastal communities by leveraging generative AI and climate science publications

There are over 250 million scientific publications and reports with an increasing rate published each year, yet many are not accessible to the public due to their technical language and content hidden behind paywalls. This project aims to leverage AI (Artificial Intelligence) and a curated database of ocean-climate literature to enable educators and students to think critically and ask challenging questions about how climate change will affect their communities, as well as empower them to engage with and apply scientific knowledge towards climate solutions. But before we start designing a system, we need a real-world understanding of who educators are and what barriers exist with finding evidence. With secondary and post-secondary educators in Atlantic Canada as an initial population, a study focusing on the context of actors and the socio-cultural-organizations is needed to understand information needs, work demands, perceptions, and how they currently search for information.

Fast Scenario Identification and Classification

Self-driving cars represent a transformative innovation in transportation, promising safer and more efficient travel. However, their development faces significant challenges, including accurate prediction, path planning, and safe maneuver execution, especially under varying driving conditions. Ensuring safety across all potential scenarios within the operational design domain is paramount. To effectively address this, we propose developing algorithms that are both cost-effective and low-latency. This would enable faster processing and identification of specific scenarios that are rare or of particular interest to autonomous vehicle developers.