Innovative Projects Realized

Adaptive Human–LLM Teaming for Scalable Cybersecurity at eSentire

This project will explore how AI can best complement human expertise in cybersecurity operations. The intern will collaborate with industry professionals at eSentire to study how tasks are currently performed, develop lightweight AI-based tools to support key workflows, and design a method to assess the impact of these tools in real-world conditions. The goal is to produce evidence-based insights and a roadmap that supports the seamless integration of AI into operational processes. For eSentire, this research will help inform strategic decisions about AI deployment, drive innovation, and support continued excellence in delivering efficient, high-quality security services.

Partnerships for Watershed Governance in Peachland, B.C.

Partnerships for Watershed Governance in Peachland, B.C. is a participatory action research initiative focused on the development of an integrated watershed co-governance model for Peachland Creek Community Watershed. This project, in partnership with the District of Peachland and Syilx communities, is designed to develop relationships, braid epistemologies, and facilitate local-level watershed governance capacity development within a mutually beneficial academic-community research framework. This project will utilize ecological governance and theory of change, community-engaged methodology and a multi-methods approach centred within a six-part governance workshop series to facilitate the collaborative design of the model. The ultimate goal of the project is to support the community of Peachland in its efforts to establish greater local-level influence in provincial watershed decision-making processes and observe the enabling conditions of a practical governance transition for the benefit of sharing lessons with other communities and community-engaged researchers working toward watershed governance development.

Industrial Vision-Based Defect Detection

Magna International Inc., headquartered in Ontario, Canada, is North America’s largest auto parts manufacturer. It supplies automotive components and systems to nearly all global automakers, including General Motors, Ford Motor Company, Volkswagen, Daimler, and BMW. With approximately 170,000 employees across 342 manufacturing operations and 91 product development, engineering, and sales centers in 28 countries, Magna has been at the forefront of introducing innovative automotive products to the market for 67 years through its cutting-edge R&D, design, and manufacturing efforts. Magna International Inc. will provide real-world manufacturing data, domain expertise, and deployment environments for vision-based defect detection systems. Magna will offer access to production facilities, historical quality data, and technical mentorship from their R&D and manufacturing teams to guide practical implementation. Magna faces critical quality control challenges including inconsistent manual inspection processes that are prone to human error and cannot scale with high-volume production demands. Currently, Magna employs visionbased defect detection systems to inspect for manufacturing errors, but these systems require optimization to improve accuracy and reliability in identifying defects across diverse product lines. This workplan outlines strategies to enhance these automated inspection capabilities and integrate them more effectively into the production workflow. Current systems struggle with detecting subtle defects across diverse automotive components, leading to costly recalls, warranty claims, and customer dissatisfaction. The company needs automated solutions that maintain quality standards while reducing inspection time and labor costs. Magna will benefit through significant cost reductions from decreased defective products, improved production efficiency, and enhanced customer satisfaction. The automated systems will enable realtime quality monitoring and data-driven process improvements. Society will benefit from safer, higher-quality automotive products, reduced manufacturing waste contributing to environmental sustainability, and advancement of Industry 4.0 technologies that can be adopted across manufacturing sectors. This research establishes a foundation for widespread deployment of AI-driven quality control systems, enhancing global manufacturing competitiveness and product reliability.

Comparison of Automated vs. Manual Sample Processing for Flow Cytometry in Diagnosing Hematologic Malignancies Using BD OneFlow™ Panels

La cytométrie en flux est une méthode essentielle pour diagnostiquer les cancers du sang tels que les leucémies, les lymphomes et le myélome multiple. Elle permet d’identifier les types de cellules impliquées grâce à des marqueurs exprimés à leur surface. Actuellement, la préparation des échantillons est souvent réalisée manuellement, ce qui peut entraîner des erreurs humaines, des délais prolongés et des résultats variables d’un laboratoire à l’autre.

Ce projet vise à comparer l’efficacité d’un traitement automatisé des échantillons à l’aide du système DUET™ (Becton Dickinson) et des panels BD OneFlow™, par rapport à la méthode manuelle traditionnelle. Ce système standardisé permet un traitement plus rapide des échantillons, avec une meilleure traçabilité.

L’étude inclura 300 patients répartis en trois groupes selon la pathologie suspectée (leucémie, lymphome ou myélome). Chaque échantillon sera divisé en deux parties et traité selon les deux méthodes, afin de comparer les performances analytiques.

Les objectifs sont de déterminer si l’automatisation permet d’obtenir des diagnostics aussi fiables, voire supérieurs, à ceux de la méthode manuelle, tout en réduisant les délais et les erreurs. Si les résultats sont concluants, cela pourrait favoriser l’adoption de solutions automatisées dans les laboratoires, améliorant ainsi la qualité et la rapidité des diagnostics hématologiques.

Investigating the Anaerobic Biotransformation of the Pesticide Chlordecone

The proposed project aims to investigate the pathway and mechanisms of the anaerobic biotransformation of the persistent organochlorine pesticide chlordecone (CLD). At the home institute, University of Toronto (Toronto, Canada), the intern has successfully identified a a trichloroethylene (TCE)-dechlorinating culture, KB-1, that can completely transform 1.5 mg/L (3.1 µM) of CLD within 200 days when supplemented with electron donors and carbon sources (methanol, ethanol and lactate). In addition, it can also transform CLD during the two subsequent refeeding cycles. To further understand the pathway and mechanisms of CLD transformation, the intern will work with Dr. Florence Popowycz and Dr. Maiwenn Jacolot at Institut de Chimie et Biochimie Moléculaires et Supramoléculaires (France) to synthesize a key intermediate 2,4,5,6,7-pentachloro-1H-indene (B1). The goal of this work is to 1) learn to chemically synthesize and purify B1 (Lyon, France), 2) gain experience with analytical techniques such as GC-MS and NMR for detecting chlordecone transformation products (Lyon, France), 3) develop synthesis methods for other intermediates, specifically compounds in family C (Lyon, France), and 4) Investigate the transformation of B1 with KB-1 (Toronto, Canada). The collaboration will combine the strengths of both institutes in environmental microbiology (home institute) and organic synthesis (host institute).

Calcul des performances d’un fuselage intégré hybride en régime transsonique

Dans les années 2000, la NASA et Boeing ont développé un concept d’avion à fuselage intégré, avec des moteurs positionnés au-dessus de la voilure, vers l’arrière de l’appareil. Cette configuration visait à réduire la traînée ainsi que le bruit perçu au sol lors des phases de décollage et d’atterrissage. Un avion en vol stable doit respecter une condition d’équilibre où la somme des moments de force est nulle, ce qui signifie que la portance compense le poids et que la poussée équilibre la traînée. Or, en raison de la position de ses moteurs, l’avion conçu par la NASA ne répondait à cette exigence qu’avec difficulté. Pour corriger ce déséquilibre, il faudrait abaisser les moteurs afin de les aligner avec le centre de masse. Cependant, cette modification soulève un autre défi : des moteurs plus bas seraient davantage exposés aux effets de viscosité de l’air perturbant l’écoulement et réduisant leur efficacité. C’est précisément ce problème que l’on cherche à résoudre: améliorer l’aérodynamisme de l’avion et la performance sans nuire à l’efficacité des moteurs.

Development of a Solvent-Free Technology for Production of High Functional Protein Powders from Oilseeds and Grains

Oilseeds and grains are considered to be excellent sources of non-animal proteins containing the appropriate essential
amino acids required for optimal human health. Conventional protein production methods involve the use of solvents,
concentrated acids and alkali that result in protein denaturation, thereby reducing the quality and functionality of the
protein ingredients. The proposed project will explore the potential of a dry or solvent free electrostatic-based
separation technique for the production of high-quality protein powders from soy and navy beans. This methodology
employs an electrostatic technique to selectively charge proteins, carbohydrates, and fibers in the bean flour and
separate them based on the magnitude and type of their charge. It preserves the bio-functionality of the protein and
averts the likelihood of toxic microbial contamination common in currently used wet processes. Advanced CERT
Canada is willing to conduct process optimization, fine-tuning and scale-up studies to move towards designing a pilotscale
plant.

Mechanochemical Chan-Lam couplings

This research collaboration explores the application of Chan-Lam coupling (expertise of the Schaper group) in mechanochemistry (expertise of the Jurca group) to explore new and more efficient preparation methods in drug development and production.

Metabolomic Characterization of Wild Canadian Haskap (Lonicera caerulea var. villosa) Germplasm

Haskap is an emerging berry crop in North America known for its unique flavour and high nutritional value. Commercial haskap cultivars have received considerable attention from researchers, however, Canada’s native wild haskap populations remain largely unstudied. This project aims to comprehensively analyze the metabolomic profiles of 150 wild Canadian haskap accessions maintained by the Fruit Breeding Program at the University of Saskatchewan. Leaf samples collected from the Canadian germplasm, as well as some advanced selections and commercial cultivars, will be analyzed at the University of Ljubljana using their established haskap metabolomic methods to identify and quantify metabolites. The resulting metabolomic profiles will be used in conjunction with existing phenotypic data to identify accessions with high potential to improve the nutritional profiles of future haskap cultivars as well as to identify biomarkers that can be used to accelerate haskap breeding at the University of Saskatchewan. This collaboration will benefit both institutions by combining the unique genetic resources of the University of Saskatchewan with the specialized metabolomics expertise of the University of Ljubljana, accelerating breeding efforts, fostering future joint research initiatives, and enhancing the training and scientific capacity at both universities.

Web-based Goal Management Training™ in older adults with cognitive impairment: a randomized-controlled trial examining feasibility and efficacy – Year two

As the Canadian population is aging, more and more Canadians will show cognitive decline. Aging and certain types of
neurological disorders is often associated with deficits in executive functions: goal maintenance, planning, task
switching and attention. These functions are critical for the maintenance of functional independence. Few validated
rehabilitation approaches for these types of deficits exist. One rehabilitation approach, Goal Management Training™
(GMT), has shown promise. In its standard implementation, GMT is led in small groups. Although this approach is
effective, it has significant practical limitations (limited accessibility and high cost). The goal of the proposed research
is to design an automated, web-based GMT training program that can be delivered remotely, to assess its feasibility in
older adults and patients with deficits in executive functions and to compare its efficacy to an active control group
that would participate in an online activity that we do not expect to improve cognition.

Health and Illness in the works of Friedrich Nietzsche and Georges Canguilhem

My master’s thesis researches the fundamental and original concepts of health and illness in the works of Friedrich Nietzsche and Georges Canguilhem, as well as the reading of Nietzsche by Canguilhem which informed his writings. I plan to study for one year (Oct. 2025 – Aug. 2026) at the Bauhaus-Universität Weimar, Germany, under the supervision of Professor Henning Schmidgen. He will guide my research on both thinkers, for whom Weimar and Paris are essential research locations. The presence of the Nietzsche Archives in Weimar is a major asset. This immersion will be my first university experience abroad, conducted entirely in German. I aim for a C1 language level and the acquisition of specific philosophical vocabulary, while also broadening my worldview and network. This stay could also establish a lasting bilateral intellectual relationship between Université Laval and Weimar. My plan includes validating my thesis topic, targeted archive consultation, and an intensive German course in Berlin, followed by my move to Weimar for courses and research. I’ve scheduled trips to the Canguilhem Archives in Paris and regular visits to the Nietzsche Archives. The university’s multidisciplinary approach will broaden my horizons. My goal is to finalize my thesis by August 2026.

ML-Enhanced SOAR Framework for Proactive Threat Response in Managed Security Operations

Security Operations Centers today grapple with overwhelming alert volumes, fragmented toolchains, and manual response processes that impede timely threat containment. Analysts must pivot between multiple SIEM and EDR consoles, manually enrich indicators, and open tickets one by one, introducing delays that adversaries exploit to dwell undetected. Moreover, static severity tags lack the nuance to prioritize truly critical events, while developing and maintaining effective response playbooks is laborious and error-prone.
This project, in collaboration with GlassHouse Systems and the University of Guelph, will deliver:
• A unified SOAR integration layer that normalizes alerts and actions across all client SIEM, EDR, threat-intelligence, and ticketing systems;
• An ML-powered risk-scoring service trained on historical incident outcomes and enriched threat data to assign every alert a dynamic priority score;
• Automated response workflows that invoke the ML scores to escalate high-risk threats, retire low-risk noise, and guide analysts through ambiguous cases with complete context.
By embedding machine learning at the core of playbook orchestration, this research will accelerate mean-time-to-detect and mean-time-to-respond, reduce false-positive workloads, and establish a reproducible, metrics