Projets novateurs réalisés

The asymmetric spin glass as a model of altered cognition in schizophrenia and the psychedelic state: A neuromagnetic and computational study

This project investigates the brain mechanisms underlying schizophrenia and altered cognitive states using advanced computational models inspired by physics and machine learning. It focuses on understanding how the brain’s activity, thought to operate in a balance between order and disorder (known as criticality), may shift away from this balance in schizophrenia and in the psychedelic state, affecting cognition. By analyzing existing brain data from patients and healthy individuals, the project aims to uncover the dynamical regimes of the brain that are linked to altered cognition and explore their implications for brain-inspired artificial intelligence systems. This collaboration between the Donders Centre for Neuroscience and the University of Montreal brings together complementary expertise. Prof. Kappen’s team at Donders contributes deep knowledge in statistical physics, machine learning, and control theory, while Prof. Jerbi’s team provides advanced neuroimaging analysis and psychiatric insights. This partnership bridges physics, AI and neuroscience, promising innovations in diagnosing and treating mental health disorders, as well as generating insights into the many dimensions of biological and artificial intelligence.

Détection acoustique d’anomalies en usine

L’usine du groupe Canam située à Sherbrooke est équipée de plusieurs perceuses magnétiques et statiques qui sont utilisées pour le perçage de pièces en acier. Ces outils doivent être opérés de manière optimale afin de limiter les bris d’équipement qui entraînent des coûts et du temps de maintenance supplémentaires. Notamment, la vitesse de rotation, la pression exercée sur l’outil et le type de mèche doivent être choisis judicieusement pour chaque tâche à accomplir. Le partenaire possède pour le moment peu de rétroaction pour s’assurer d’une utilisation optimale des outils. Cependant, les ouvriers expérimentés sont en mesure de déterminer l’état d’un outil à partir du son émis par celui-ci. Cette observation demeure néanmoins subjective, et gagnerait à être mesurée et analysée grâce à des méthodes numériques avancées. Ce projet vise à concevoir un système automatique d’écoute des perceuses pour fournir une rétroaction aux opérateurs moins expérimentées et garantir une utilisation optimale des outils.

L2M – Otherside Health

The project aims to provide accessible, empirically-guided, and population-specific mental health interventions. These interventions will be co-created with the populations who will benefit from them, using a participatory action framework developed by applicant Kev Kokoska’s PhD research with incarcerated people. The core idea is to tailor and repurpose the mental health tools that have proven effective in the hardest setting (prison) so that other hard to reach or “treatment resistant” communities may also benefit. Our mental health market research to date tells us that tailored (population-specific) approaches to mental health are of significant need, especially in historically marginalized communities. The main commercialization challenge we have faced is clarifying the difference(s) between ‘customers’ and ‘users’. Therefore, this will be the primary focus during this Lab2Market Validate process. We wish to better understand the needs of customers (institutions/organizations) and how solving their institutional pains will promote mental wellness in our users (specific segments of the ‘general public’).

Parameterized Pulse Encoding for Quantum Machine Learning

Chemical property predictions using quantum machine learning (QML) lie at the intersection of machine learning, quantum computing, and computational chemistry. QML models often use parameterized quantum circuits (PQCs) that abstract gate-level quantum operations but offer limited flexibility in adjustable parameters. To enhance QML model performance and generalizability, incorporating pulse-level operations, which lie below gate-level in the quantum computing stack, is crucial. These operations enable more adjustable parameters and finer control, potentially improving robustness against noise on real quantum hardware.

We hypothesize that integrating pulse-level operations into QML models will improve their ability to predict chemical properties like bond dissociation energies through regression tasks. The goal is to develop hybrid quantum circuit-pulse learning algorithms to optimize both pulse- and gate-level parameters. We will use quantum-mechanically calculated chemical data to train and test the models, investigating how training data size affects generalization error.

Additionally, the project will evaluate whether hybrid circuit-pulse learning offers advantages over standard quantum circuit learning in accurately predicting chemical properties. By addressing PQC limitations and exploring pulse-level operations, this work aims to advance QML’s application in computational chemistry, paving the way for more powerful and adaptable models.

Sii T’ax Development Corporation Five-Year Strategic Plan

This project is a strategic planning project designed to transfer skills and capacity building in the form of a five-year strategic plan for the Sii T’ax Economic Development Corporation. This project will also include an assessment of the current business units of the Sii T’ax Economic Development Corporation. Based on recent and current ISIS projects with the four villages of the Nisga’a First Nation, ISIS proposes to expand its economic development planning initiatives into a capacity building, skills transfer and mentoring role with the board of directors at the Sii T’ax Economic Development Corporation. In its highest-level form, this would involve an ISIS student spending an initial 2 months to become familiar with the Sii T’ax Economic Development Corporation and designing a five-year strategic plan. At the end of this 2-month period, ISIS would work on recommendations of implementation for the growth of the business units of Sii T’ax Economic Development Corporation.

Strengthening the Transition to Sustainable Agricultural Practices in Quebec: Understanding the Barriers, Mobilizing Stakeholders and ESG Strategies

The project aims to support Terre à table, an organization committed to sustainable agriculture, by deepening the understanding of the economic and environmental challenges related to agri-environmental practices in Quebec. The intern will conduct research to identify barriers to farmers’ adoption of sustainable practices and ways to overcome them. It will also analyze the sustainability strategies of food companies to foster beneficial partnerships between producers and businesses. At the same time, it will study the specific needs of other actors in the organization’s areas of action, such as indigenous communities . This project will enable Terre à table to better support producers in the transition to a more environmentally friendly agriculture, while strengthening its links with other players of the agri-food value chain.

Evaluating the Potential of Stationary and Mobile Energy Storage to Improve Energy Flexibility in Nova Scotia

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Agent conversationnel intelligent évaluant la crédibilité de signes cliniques en santé mentale

Le projet en développement consiste à créer un agent conversationnel intelligent destiné à soutenir les professionnels de la santé mentale dans leur pratique quotidienne. Cet outil novateur vise à faciliter le processus de diagnostic différentiel, une étape cruciale pour déterminer avec précision les troubles de santé mentale des patients. Grâce à une intelligence artificielle avancée, l’agent est capable d’interagir de manière naturelle et intuitive avec les professionnels, en leur fournissant des rétroactions adaptées à leurs besoins cliniques. Conçu pour s’intégrer harmonieusement dans le flux de travail des professionnels, cet outil peut contribuer à optimiser le temps consacré à l’analyse des cas complexes. Tout en respectant la confidentialité des données et en répondant aux normes les plus strictes en matière de sécurité, ce projet promet d’être une ressource précieuse pour les professionnels de la santé mentale, en les épaulant dans leur mission de fournir des soins de qualité.

Reconfigurable Ferromagnetic Liquid Crystal Elastomer Composites

Liquid Crystal Elastomers (LCEs) are shape-changing polymers that contain asymmetrical liquid crystal molecules chemically bonded to the polymer chains in an ordered fashion. Heat and light can be used to change this molecular order, which results in the polymer changing its shape. LCEs show promising applications in remotely controlled soft robotics and actuators, such as microscopic drug delivery systems and soft actuators in microelectromechanical systems. LCEs are thermosets. Once the microstructure is locked-in by crosslinking, it cannot be reprocessed and reprogrammed, limiting its scope to the function(s) it was designed for. Additionally, since these materials cannot biodegrade or be recycled, they are disposed of at their end-of-life. Dr. Mohand Saed’s group at the University of Cambridge has developed LCEs containing molecules with exchangeable chemical bonds (xLCEs), addressing this sustainability issue. The performance of these xLCEs as soft robots and actuators may be enhanced by the incorporation of ferromagnetic nanoparticles, developed by Dr. Hamed Shahsavan’s group at the University of Waterloo. Magnetic nanoparticles show promise to incorporate magneto-responsive locomotion, molecular reconfiguration, and unlock novel modes of shape change actuation. In the proposed collaboration, combining these enhancements will realize a novel, customizable shape changing material for developing soft robots, with increased sustainability.

Large-scale scenario modelling of invasive plant spread across Canada

Invasive species are organisms that arrive in a new environment and quickly dominate the landscape. They present one of the most important threats to local plants and animals; this is especially the case for invasive plant species, which comprised almost a tenth of Canada’s vascular plant life in 2008. Despite the abundance of invasive plants, the ways they spread can be difficult to predict due to a lack of information on the various factors at play. However, due to the possibility that certain factors promoting spread – such as habitat suitability and human activity – might be common among invaders, general simulation models could allow new invasions to be forecast when monitoring information is limited. As such, my research aims to model how climate change and Canada’s most damaging invasive species will alter future plant health within protected areas nationwide by (1) weighing the various factors currently driving invasive plant spread and (2) producing maps of forecasted spread under various future climate scenarios. My findings will be made available to conservation practitioners across the country to (1) understand the risk versus resiliency of current protected areas to invasion and (2) prioritize future land acquisition for protection under climate change.

Field measurement of sustainable transportation phenomena

The objective of this project is to comprehensively measure the experiences and circumstances around commuting to large employers (e.g., in this case Carleton University). The motivation is to identify barriers and incentives to commuting without a car. Two interns from University of Savoy Mont Blanc’s Solar Academy master’s program will visit Carleton for the summer of 2025. In brief, their project focuses are as follows.
The first intern will develop and deploy methods to measure modal share of commuters to campus. To achieve this, the first intern will design and implement one or more of the following common methods: a survey, manual observation, and/or computer vision. (Multiple complementary methods would be used for validation if deemed necessary after a literature review).
The second intern will measure air quality and noise along key pathways for pedestrians and cyclists who are commuting to campus. In particular, Queen Elizabeth Drive is scheduled to be partially closed to cars for part of the summer, thus the impact of cars on QED can be assessed. To achieve this, the intern will develop a measurement plan (e.g. measure in 100-meter intervals along the road on several days (to capture a variety of wind and traffic conditions).

Méthodes d’apprentissage automatique pour l’étude d’indicateurs d’urbanisme intelligent.

Les municipalités sont confrontées aux différents défis modernes liés à la vie en société. Afin de les aider à prendre des décisions éclairées, les décideurs disposent de plus en plus de données qui leur sont disponibles. Un problème important est que ces données proviennent de différentes sources et sont présentées dans différents formats. Cela rend leur utilisation difficile. De plus, bien qu’il existe des méthodes d’intelligence artificielle efficaces, leur implémentation est complexe et nécessite une adaptation aux problèmes et aux données spécifiques. Cette recherche propose le développement d’un outil d’étude d’indicateurs pour les municipalités. Doté d’une interface graphique simple, le rôle de cet outil logiciel sera de gérer les données extraites de différentes sources, d’appliquer des méthodes d’intelligence artificielle telles que l’apprentissage automatique pour effectuer des prédictions, des classifications, des regroupements (clustering) ainsi que l’analyse et la présentation des données relatives aux indicateurs ciblés. Ces indicateurs seront composés de plusieurs variables. Par exemple, un indicateur de vitalité des quartiers de la municipalité pourrait inclure des variables telles que le taux d’occupation des logements, la valeur moyenne des logements, le niveau de scolarité des citoyens, etc. En fin de compte, l’outil est utilisé pour générer automatiquement un rapport au format PDF, comprenant des graphiques, des tableaux et des textes, dans le but d’aider les décideurs (gestionnaires et urbanistes) de la municipalité dans leurs prises de décision. Développé au cours des prochains mois, ce travail sera un point de départ pour poursuivre dans le futur une exploitation intelligente des données d’une ville à l’aide d’un outil à base d’intelligence artificielle et une interface simple.