Innovative Projects Realized

Data-driven inventory management improvements

Bit Service Company Ltd. is a Saskatchewan manufacturer and distributor of mining products and equipment. They specialise in design, manufacturing, and deployment of carbide and speciality alloy tipped cutting bits for underground mining. The project directly supports Bit Service Company Ltd. ability to better monitor inventory levels and predict sales in the future based on historical trends.

Mapping clusters to behavior profiles

Mapping clusters to behavior profiles – This project directly addresses the need to build a bidirectional predictive model: one that can not only infer motivational traits from data but also simulate likely outcomes from known psychometric inputs. This level of interpretability and personalization is important to introduce a layer of human-centered modeling previously unavailable in typical business operations.

Going Up in Flames – Impact of Being Exposed to Beliefs-Altering Residential Fires on Households’ Financial Holdings and Career Path

Canadian households face significant financial risks, including inadequate retirement savings and not being financially resilient to unexpected adverse events. An understanding of why households bear these risks and how their risk attitudes shift with economic shocks is thus critical. Although beliefs and risk perceptions are at the root of households’ financial decisions, the literature is silent on how first-hand experience of “life-changing” events affect individuals’ employment, financial holdings, and insurance policies. Therefore, this project seeks to answer the question: Do “beliefs-altering events”, defined as severe, one-off, and non-strictly financial losses, impact Canadian households’ financial holdings? Our partner, a major Canadian financial institution, will gain a deeper understanding of how households adjust their financial strategies after experiencing unexpected shocks. The quantification of how beliefs-altering events encourage individuals to reallocate financial assets (including insurance policies) or change career paths will allow our partner to better support affected clients.

Business Strategy

This project aims to accelerate the development and adoption of Cropmind Inc.’s AI-driven precision farming solution by focusing on product refinement and market expansion. The intern will enhance software usability by analyzing user feedback, developing prototypes, and conducting product testing to optimize AI integration for farmers. Additionally, they will support business development by engaging with potential customers, executing strategic marketing initiatives, and conducting on-site demonstrations. By improving the platform’s functionality and expanding its market reach, this project will help establish Cropmind as a leader in AI-powered precision agriculture, driving innovation and efficiency in the farming sector.

Impact des comparaisons indirectes dans la prise de décision par la CDA-AMC et l’INESSS

Les comparaisons indirectes sont fréquemment utilisés pour évaluer l’efficacité d’un nouveau traitement par rapport aux options thérapeutiques déjà disponibles sur le marché canadien, une étape primordiale dans l’évaluation des technologies de la santé (ETS) avant d’obtenir un remboursement. Toutefois, une incertitude réside sur leur impact dans la prise de décision par ces agences. Ainsi, l’objectif de ce projet est d’identifier si des tendances existent entre les caractéristiques de la comparaison indirecte utilisée et son influence sur la recommandation posée par les agences d’évaluation canadiennes, soient l’Agence des médicaments du Canada (CDA-AMC) et l’Institut national d’excellence en santé et en services sociaux (INESS). Parmi les éléments pris en compte figurent le type de comparaison utilisée, la condition médicale pour laquelle le produit fait l’objet d’évaluation, les limites identifiées par l’agence, ainsi que l’entité qui a conduit l’analyse, soit la compagnie ou l’agence elle-même. Enfin, les résultats de ce projet permettront d’améliorer les conseils offerts aux compagnies pharmaceutiques souhaitant un obtenir un remboursement de leur médicament sur le territoire canadien, facilitant ainsi l’accès des patients à leur traitement sans fardeau financier.

Disinfection of wastewater effluent in low resource and humanitarian contexts

Wastewater is one of the primary point-source contaminants polluting freshwater sources, including shallow groundwater sources. Over 80% of wastewater worldwide is neither collected nor treated and more than 70% of sewered wastewater from human activities is discharged without any form of pollution control, increasing the risk of waterborne diseases. This project, in collaboration with Centre for Affordable Water and Sanitation Technology (CAWST) will address a critical public health issue by evaluating potential onsite wastewater disinfection solutions integrating UV light-emitting diode (LED) technology for disinfecting wastewater. Experimental trials using UV LED reactors that are manufactured in Canada, will be conducted at the University of British Columbia to evaluate disinfection performance and validate the predictions of a computational tool. The study will analyze UV disinfection dose-response curves, inactivation rates, and water quality parameters to assess the efficacy of UV LED technology for inactivating E. coli and other contaminants. Partner organization, CAWST, with expertise in water, sanitation and hygiene options using simple, affordable technologies, will work with the intern on the experimental design, engineering considerations, testing protocols, results interpretation and data analysis. Together, we aim to assess whether this novel disinfection solution is appropriate for sanitation in a disaster relief and humanitarian contexts.

Development of algorithms to improve microflow analysis

Our primary mandate at API is to advance basic research and innovation to commercialization by providing access to world-class industry expertise, services, and infrastructure. Our activities focuses on engaging and supporting drug discovery and development initiatives, ensuring compliance with regulatory standards and driving innovation and commercialization through collaborative research and clinical studies. The investigation of extracellular vesicles (EV) as the next frontier for diagnostic evaluation is underway across the globe. There is a myriad of techniques available to interrogate extracellular vesicles, but the most versatile by far is EV flow cytometry (evFC). This technique can assess millions of potential cell fragments from microlitres of biological material such as plasma, urine or cerebrospinal fluid. From these investigations, we can begin to unravel potential new biomarkers when intact tumor cells may simply be too rare to monitor. Similarly, we can monitor patient response to medical treatments without requiring excessive amounts of material. In addition, we can detect and monitor viral and bacterial infections due to the sub-micron resolution of the technique.
However, with any technique that enhances resolution, detection of the appropriate signals becomes critical. Identifying the true positive signal from the “negative” background has traditionally been a subjective protocol with cell-based flow cytometry. This process, called “gating” involves selecting a subset of events from all events collected during a flow cytometry experiment for further analysis or data presentation. This includes general clean-up of the data, such as removing dead and dying cells or events consisting of multiple cells, as well as isolating your target cell population using their characteristic size, granularity, and expression of various cell markers. Proper gating, along with smart panel-building, can make your data easier to interpret and more publication-ready. The development of machine-learning based approaches for dynamic gating and instrument calibration (refractive index and size) will not only enhance the analysis of small particle evFC data, but also significantly increase the throughput capability of analyses of large datasets from months to weeks or even days.

Intelligent Survey Technologies for Education: A Research, Design and Development to Enhance Usability, Data Collection, and Adaptive Logic

Xello is a leading college and career readiness platform designed to engage K-12 students in career exploration, academic planning, and skill development. It provides educators with data-driven insights to support student success, helping school districts make informed decisions.
To maintain its market leadership and enhance user experience, Xello seeks to improve its survey platform. The current system has limitations in usability, accessibility, and data management, impacting the effectiveness of surveys in collecting meaningful insights. Key challenges include the lack of safe survey deletion, limited printable survey functionality, restricted survey distribution to alumni, and the absence of file attachment support for responses. Additionally, complex survey branching logic needs improvement for better customization and engagement.
The Xello team aims to collaborate with WIMTACH’s applied research team to conduct an in-depth analysis of these challenges, explore innovative solutions, and develop an enhanced survey management system. This partnership will leverage research-driven methodologies and technological expertise to create a more efficient, user-friendly, and data-rich platform.
This project will drive innovation by optimizing survey management, streamlining data collection, and integrating advanced features such as multimedia attachments and dynamic survey logic. By modernizing these tools, Xello will enhance the efficiency of educators, improve student engagement, and expand survey capabilities for long-term data-driven decision-making. The anticipated benefits include increased adoption of Xello’s platform, improved educator workflows, and stronger data insights that support student success, ultimately reinforcing Xello’s position as a premier educational technology provider.

XEOS : Détection par IA de défauts sur des photographies aériennes

XEOS : Détection par IA de défauts sur des photographies aériennes

Principales activités du partenaire :
XEOS Imagerie est une entreprise spécialisée en photographie aérienne, relevés lidar et cartographie par intelligence artificielle. Elle a développé une grande expertise en cartographie par intelligence artificielle notamment à partir de nuages de points lidar en 3 dimensions et de photographie aérienne.

Problématique et avantages escomptés du projet :
La validation des reconstructions 3D des bâtiments est facilitée par l’examen de photographies aériennes dans les mêmes zones. Celles-ci permettent de mettre en contexte les surfaces des bâtiments et autres structures détectées. Elles permettent aussi d’identifier des problèmes ayant potentiellement des conséquences sur les reconstructions 3D et leur interprétation.
La présence de défauts d’acquisition dans la photographie aérienne réduit la qualité d’interprétation ou rend la photo inutilisable. La détection manuelle de ces défauts dans des milliers d’images prend du temps et est laborieuse. Il serait utile d’automatiser et d’optimiser ce processus. Plusieurs approches s’offrent à nous.
La définition du problème de la détection d’objets se divise essentiellement en deux parties distinctes : où se trouvent les objets dans une image donnée (localisation des objets) et à quelle catégorie appartient chaque objet (classification des objets). Par conséquent, les pipelines des modèles traditionnels de détection d’objets peuvent être divisés en trois étapes : sélection de la région informative, extraction des caractéristiques et classification. Les réseaux de neurones permettent d’effectuer ces trois étapes en même temps.
XEOS Imagerie possède déjà à l’interne une grande expertise en programmation en intelligence artificielle mais désire s’entourer de plusieurs stagiaires afin d’augmenter sa capacité de développement de produits.

Deformable Image Registration for Multimodal Radiotherapy Treatments in Gynaecological Cancers: External Beam and Brachytherapy

The Radiation Medicine Program at the Princess Margaret Cancer Centre delivers curative radiation therapy (RT) to cancer patients, including those with gynecological cancers, using a combination of external beam radiation therapy (EBRT) and brachytherapy (BT). A key clinical challenge is the accurate accumulation of radiation dose delivered across these modalities. Current methods rely on deformable image registration (DIR), which is hindered by large uncertainties due to anatomical changes caused by the BT applicator, variable bladder and rectum filling, and tumor shrinkage during treatment (Fu et al., 2023). These limitations reduce the accuracy of longitudinal dose accumulation and compromise treatment effectiveness. This project addresses that challenge by developing deep generative models to remove BT applicators from MR images, enabling accurate DIR and dose mapping between EBRT and BT sessions. The partner organization will benefit clinically by improving treatment precision and enabling better-informed re-irradiation strategies. Socially, the project supports safer, more effective cancer therapy, while economically, it may reduce planning errors and treatment complications, ultimately improving healthcare resource efficiency. By advancing AI-driven radiotherapy tools, this project also enhances the partner’s leadership in integrating machine learning into clinical cancer care.

Thales : Briefing de Situation Intelligent

Thales : Briefing de Situation Intelligent
Principales activités du partenaire :
Thales Canada conçoit et met en oeuvre des solutions reposant sur des hautes technologies. L’entreprise offre des capacités de pointe dans les secteurs de l’aviation civile, de la défense, de l’identité et de la sécurité numériques.
Problématique et avantages escomptés du projet :
Ce projet vise la création d’une capacité de génération de rapports structurés à partir de notes non structurées, en utilisant des LLM (Large Language Model). Plus précisément, la solution développée prendra en entrée (x) des traces écrites et désorganisées, transcrites de source audio, notes papier et/ou digitales, afin de générer un rapport structuré (y), spécifique au domaine. Typiquement, de tels rapports représenteront de façon concise et organisée le contexte, les événements clés et les conclusions (recommandations, actions, etc.) associés.
Cette capacité permet d’abord de faciliter la création de rapports requis dans plusieurs domaines et diminue la quantité d’information perdue ou oubliée. Cette capacité représente un composant dans une suite de capacités GenAI d’aide à la décision dans les domaines critiques.

Monitoring hair surface chemical modifications using atomic force microscopy

Since the beginning of recorded time, humans have been developing ways to make themselves more beautiful or
otherwise change their appearance. The hair-care industry itself has a huge global economic power: its estimated total
value is $47B annually. However, beauty does not come without a price: methods currently being used for hair
colouring and styling damage hair greatly. More importantly, they involve treatments that have negative effects on
human and environmental health.
To reduce the toxicity of hair-care treatments, SLI Beauty is developing new hair-surface chemical modification
techniques. I have partnered with them to bring my expertise at biophysical characterization to assess the success of
these surface modifications, and to develop new treatment modalities. With dedicated time spent onsite in the SLI
Beauty labs, I have the opportunity to bring my critical skills to help develop marketable products for this expanding
company.