Innovative Projects Realized

The Diagnostic Potential of Co-rotating Interaction Regions in Dense Hot-Star Winds

Wolf-Rayet (WR) stars exhibit strong, high-velocity winds with small-scale stochastic clumps and large-scale structures like Co-rotating Interaction Regions (CIRs). CIRs are spiral-shaped density enhancements, which propagate through the wind and induce variability in spectropolarimetric signals. WR6 (EZ CMa, WN4b) is a well-studied target with a stable 3.76-day periodicity in its wind variations. Recent observations, including linear spectropolarimetry, reveal distinct patterns in the Stokes Q-U plane, suggesting a complex, asymmetric scattering envelope.

Current modeling efforts, such as those by Dr. Richard Ignace, provide proof-of-concept frameworks for interpreting spectropolarimetric data. However, these models require refinement and validation through deeper observational insights. The proposed research seeks to advance the synergy between observations and modeling to uncover the mechanisms behind these variations, a critical step in understanding massive-star evolution and feedback.

Projet de recherche – Occupation temporaire d’espaces urbains vides à Montréal. Augmentation des campements urbains et de l’urbanisme temporaire en situation de crise du logement abordable.

Alors que la crise du logement atteint des niveaux critiques à Montréal, la gestion des espaces vacants soulève d’importants enjeux sociaux et urbains. Récemment, divers types d’occupations temporaires émergent : certaines, portées par des institutions ou des initiatives citoyennes, visent à revitaliser l’espace public à travers des projets culturels, sociaux ou économiques, tandis que d’autres, comme les campements urbains, résultent de la précarisation croissante et sont souvent perçues comme problématiques par les autorités. Ces occupations, bien que différentes, révèlent des tensions autour de la légitimité de l’appropriation de l’espace urbain et des politiques qui en régulent l’usage.
Ce projet de recherche analyse les représentations, discours et pratiques entourant ces occupations, en mettant en lumière les tensions, rapports de pouvoir et mécanismes de légitimation qui influencent leur reconnaissance ou répression. À travers une enquête ethnographique auprès des usagers, collectifs citoyens et acteurs associatifs, il donnera la parole aux personnes concernées pour mieux comprendre leurs expériences et stratégies face aux cadres institutionnels. En approfondissant ces enjeux, cette recherche contribuera à une réflexion plus large sur les politiques urbaines et la gestion de l’espace, afin d’intégrer la diversité des usages et besoins dans un contexte de transformation accélérée.

Non-invasive assessment of early molecular response and detection of minimal residual disease in diffuse large B-cell lymphoma (DLBCL) using cell-free methylomes

Research at the Lymphoma Lab is driven by the observation that the outcomes of lymphoma are extremely variable with some patients seem cured or enjoying long remissions while others experience relapse. Treatments that directly target the underlying therapeutic vulnerabilities of a patient’s lymphoma remain unavailable [1]. Lymphoma Lab wants to change this. A comprehensive understanding of the biological underpinnings of lymphoma can lead to improved therapies for patients. One promising approach is detecting mutations in circulating tumor DNA (ctDNA) to assess disease status non-invasively [2]. However, its effectiveness is limited by the small number of mutations detectable in targeted sequencing panels, reducing its sensitivity. Enzymatic Methyl-seq (EM-seq), a novel technique, enables the non-invasive profiling of tumor methylomes. Unlike mutation-based methods, EM-seq can analyze thousands of alterations without being restricted by predefined sequencing panels [3]. They hypothesize that methylomes from EM-seq will allow identification of lymphoma specific methylation signatures that can be quantified and tracked over time to determine treatment response. Lymphoma Lab aims to clarify the mechanisms of lymphoma pathogenesis, tumor evolution, and treatment resistance. The lab’s overarching goal is to enhance patient outcomes by deepening the understanding of treatment response variability and advancing personalized therapeutic approaches for lymphoma. More information on the lab can be found at http://kridel-lab.ca/Research/.

Improved Order Computation in Class Groups of Real Quadratic Fields

Cryptography is an important tool for safeguarding our data from attackers. The security of several modern cryptosystems relies on unproven properties of an algebraic structure called the class group of an algebraic number field. In the absence of proofs, tabulating class groups in order to generate numerical evidence of these unproven properties remains the best way to enhance our confidence in their truth and the security of the related cryptosystems. However, tabulating class groups in all but the simplest types of number fields remains a significant computational challenge. This project will devise improved algorithms for computing the order of an element in the class group of a real quadratic field, the simplest case of number fields where these challenges manifest. Order computation can be considered as a special case of computing the full class group. The results will be a significant step to improving algorithms for class group computation, eventually leading to the extended class group tabulations required to bolster our confidence in security claims of related cryptosystems.

ESROP – KMUTT – Context Aware AI Music Therapist for Elderly with Neurodegenerative Diseases

Neurodegenerative diseases like Alzheimer’s and Parkinson’s cause cognitive decline, motor impairment, and emotional distress. While pharmacological treatments exist, music therapy has shown promise in alleviating symptoms. This project enhances music therapy using AI, making interventions more accessible and personalized. This project aim to develop an AI-powered wearable system that personalizes music therapy based on emotional state and physiological responses. Using machine learning and biometric monitoring, the system will analyze user data to curate and deliver tailored music therapy in real-time.

Passive Temperature-Responsive Solar Energy Switch for Heat and Electricity Generation

Harnessing Solar Power for Smarter Energy Use:
This project introduces a passive solar energy switch that automatically adjusts how it uses sunlight based on temperature, without electricity or moving parts. It relies on a Fresnel lens that focuses sunlight. When temperatures are low, water condenses on the lens, causing solar transmission and heating. When temperatures rise, the water evaporates, restoring the lens’s ability to focus sunlight for efficient solar electricity generation.

Why It Matters:
Heating and cooling account for over half of global electricity use. Traditional systems consume large amounts of energy, leading to high costs and environmental impact. This technology offers a self-regulating, cost-effective, and scalable alternative for energy-efficient buildings and solar power systems.

Real-World Applications:

1. Smart Windows that adapt to sunlight, reducing energy use.

2. Solar Concentrators that enhance electricity generation.

3. Off-Grid Energy Solutions that work in varying conditions.

By integrating natural processes like condensation into solar energy management, this innovation enhances sustainability and efficiency in everyday applications.

L2M – Development of Self-Powered Portable Devices for Simultaneous Water Disinfection and Pollutant Removal Through Piezophotocatalysis

This project aims to provide clean drinking water to low-income families in remote areas by developing a self-powered, portable ceramic water filter. While the filter has shown strong pollutant removal in lab tests, real-world testing is needed to confirm its effectiveness. This project will focus on pilot testing, refining the product based on user feedback, and developing a strong market strategy. By partnering with organizations, we can accelerate commercialization, secure funding, and ensure successful market entry. The project will benefit partners by creating opportunities in the water filtration industry while improving public health through affordable and sustainable clean water solutions.

Confection des horaires des pilotes du St-Laurent central

Le projet consiste à développer une méthodologie novatrice, de type heuristique, de construction d’horaires annuels des pilotes de la Corporation des Pilotes du St-Laurent central. L’algorithme de construction d’horaires doit tenir compte de plusieurs contraintes tant au sein de la Corporation des Pilotes du Saint-Laurent central que celles imposées par l’Administration de Pilotage des Laurentides (APL). L’algorithme d’optimisation doit tenir compte du volume de circulation des navires de différentes classes tout au long de l’année et des qualifications des pilotes avec des contraintes mensuelles moyennes sur le minimum de pilotes pour chacune des classes de navires, qui correspondent aux qualifications des pilotes. Cet algorithme de construction des horaires sera incorporé dans un logiciel opérationnel au sein de l’entreprise et il permettra la construction des horaires ainsi que la réalisation de simulations en faisant varier les paramètres en lien avec les contraintes. Ce logiciel se veut un outil de planification et de gestion des opérations courantes de la Corporation.

Design Principles of Biological Networks

This project aims to uncover the design principles of biological networks, such as blood vessels in the kidney and brain, by developing automated tools to analyze high-resolution 3D images of human embryo vasculature. Using advanced imaging techniques and deep learning, the research will create efficient methods to study how these networks efficiently transport nutrients and information. The findings could lead to better treatments for diseases like stroke and kidney disease, while also inspiring innovations in engineering and technology, such as smarter infrastructure or more efficient communication systems. By collaborating with Dr. Alain Chedotal’s lab in Paris, the project will bring unique datasets and expertise to Canada, strengthening international research partnerships and positioning Canadian institutions as leaders in computational biology and bio-inspired design.

Metabolomics & Cancer Biomarkers

Renal cell carcinoma (RCC) is a common and aggressive form of kidney cancer, often diagnosed at advanced stages due to the lack of early symptoms. This project aims to improve RCC diagnosis by identifying metabolic biomarkers through a multiomics approach, integrating metabolomics and transcriptomics data.

Using advanced mass spectrometry (LC-MS) and computational tools like CAT Bridge, we will explore gene–metabolite interactions to uncover key regulatory pathways in RCC progression. By treating cancer staging as a progression model, we seek to establish causal links between genetic changes and metabolic alterations. This research will contribute to early detection, personalized treatment strategies, and non-invasive diagnostics, ultimately enhancing patient outcomes.

Through collaboration between Chang Gung University in Taiwan and the University of Alberta in Canada, this project combines cutting-edge analytical techniques and computational models to advance biomarker discovery and precision medicine in RCC research.

Mise au point d’un nouveau procédé de fabrication de laser

Les travaux du stage seront effectués à l’Institut interdisciplinaire d’innovation technologique de l’Université de Sherbrooke au sein du groupe de recherche de la professeure Gwenaëlle Hamon qui se spécialise sur les procédés de micro-fabrication et caractérisation des dispositifs optoélectroniques tels que les panneaux solaires, phototransducteurs, lasers, etc. Le stage proposé vise la mise au point d’un nouveau procédé en salle blanche pour la fabrication de laser émettant à 1550nm sur substrat InP en partenariat avec une compagnie québécoise.

Geotab Ace: Enhancing Fleet Intelligence with Retrieval-Augmented Generative AI for Natural Language Data Interaction

Geotab is a global leader in IoT and connected transportation, providing data-driven insights for fleet management. Collecting over 4 billion data points daily from 4.5 million connected vehicles, Geotab’s AI Platform team focuses on leveraging machine learning to enhance data accessibility, fleet intelligence, and operational efficiency. A key challenge is improving user interaction with complex datasets, as traditional methods require manual report analysis and complex query navigation. To address this, Geotab is developing Geotab Ace, an AI assistant integrating Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to enable intuitive, natural language-based data access. This project aims to enhance query accuracy, data retrieval efficiency, and user experience. The anticipated benefits include improved customer decision-making, reduced support overhead, and optimized fleet operations. Additionally, AI-driven fleet intelligence contributes to safer, more efficient transportation, reducing costs and emissions through predictive maintenance and route optimization. The project also advances practical AI applications in telematics, pushing the boundaries of LLM-based conversational AI in real-world, data-intensive environments. By making data-driven decision-making more accessible, this research benefits both Geotab and the broader industry, driving AI innovation in fleet management and IoT analytics.