Innovative Projects Realized

Analyse du rôle et de l’impact des critères substituts dans l’homologation et le remboursement des médicaments anticancéreux au Canada.

Lorsqu’un nouveau traitement est évalué dans les études cliniques, des critères cliniques sont utilisés pour mesurer son efficacité en fonction de l’état du patient, notamment comment il se sent, fonctionne ou survit. Cependant, ces critères peuvent nécessiter un suivi prolongé avant de fournir des résultats concluants dans la recherche clinique en oncologie. Pour accélérer l’évaluation des traitements, des critères substituts peuvent être utilisés. Ce sont des paramètres intermédiaires qui permettent de prédire plus rapidement l’effet d’un médicament sans attendre des résultats à long terme, comme la survie globale. Par exemple, la mesure de la maladie résiduelle minime peut donner une indication précoce de l’efficacité d’un traitement contre le cancer. Ainsi, l’intégration des critères substitutifs dans les demandes de remboursement auprès d’agences comme l’Agence des Médicaments du Canada et l’Institut National d’Excellence en Santé et Services Sociaux au Canada est un enjeu clé. Elle permet de faciliter l’accès aux anticancéreux tout en assurant leur valeur clinique ajoutée pour les patients. Dans ce contexte, ce projet de stage vise à analyser comment les critères substituts sont pris en compte dans les demandes d’homologation et d’évaluation du remboursement des médicaments anticancéreux au Canada, afin d’identifier les tendances, les défis et les opportunités.

Quantum Black Holes

For a long time, scientists have been trying to unify the theory of gravitation with quantum physics. Quantized gravity is necessary to correctly describe phenomena where huge masses combine with tiny dimensions such as at the big bang or in black holes. The latter can be observed and thus can serve as an ideal testbed for theories of quantum gravity. Although full quantum gravity still hasn’t been achieved, quantum black holes have been studied in a semi-classical approach, where quantum corrections of matter are taken into account through the energy-momentum tensor, its classical version being replaced by an expectation value of its quantum counterpart. In this project, we aim to continue on this path. We shall construct quantum charged and spinning black holes in the context of non-linear electrodynamics and we will explore their thermodynamic behaviour. This work is of high importance for the field of theoretical physics and will be highly beneficial for participating institutions since it presents the most current research and can bring fascinating discoveries in the branch of black hole physics.

Optimizing Resource Allocation in Satellite-Assisted Hybrid Quantum–Classical Networks

This project brings together ÉTS Montréal and the American University of Beirut to design smarter ways for satellites and ground stations to share network resources so that next generation of quantum secure communications can run in a optimized way. Using quantum computer simulations, the intern will test new matching and learning algorithms that decide, in real time, which links and bandwidth each user should get, balancing speed, reliability, and security. The work will give ÉTS practical tools and fresh data for Canada’s emerging quantum network programs, while AUB gains hands on experience with advanced network design; strengthening both partners and helping move the world closer to a truly global, secure internet.

AI-assisted Recommendation System for False Positive Reduction at Security Operations Centers

Security Operations Centers (SOCs) are responsible for detection and review of malicious interactions. The SOC issues tickets for interactions that are considered suspicious or threatening. These tickets are then inspected by analysts for approval. For sake of safety, this ticketing system often issues too many “false positives”, i.e., it alerts for interactions that are not really threatening. While this keeps the security level high, it can cause analyst fatigue due to high volume of unnecessary ticket reviews. This project aims to develop an AI-assisted system to refine detection mechanisms at SOCs and reduce the issue of unnecessary alerts. This can contribute significantly in enhancing SOC efficiency by both decreasing the number of false positives and reducing the number of reports being processed by the analyst in a certain time period.

Exploring How Mathematics and Literacies Homework is Taken Up the Context of After School/Summer Programs

Our current research is focused on homework policies, how teachers enact such policies, and how homework impacts family life. Extending that work into this proposed MITACS research project, we aim to examine after-school and summer programs and how such programs are connected to schools and homework. This project will provide insights into how mathematics and literacy are taken up outside of the context of school. The partner organization will provide a space for an after school/summer program where an intern will work with children in mathematics and literacy. The project will recruit an intern to help organize and support new and existing programs with the partner organization. The proposed research aims to: a) investigate how mathematics and literacies homework is taken up the context of after school/summer programs (b) examine students’ perceptions of what constitutes math and literacy and (c) support learning during the summer months.

Research intern for leaf morphodynamics

The ability for movement to adjust posture and growth in response to environmental stimuli is important for plants as sessile organisms. This project aims to understand how mechanics regulate plant movement by studying leaf movement in Arabidopsis. This research combines cutting-edge imaging and mechanical modeling techniques from both the host institution, Université de Montréal, and the home institution, Nara Institute of Science and Technology, to explore the driving mechanism of the movement from a multi-scale mechanical perspective. This project strengthens international collaboration between the two institutions and promotes research in multiscale morphodynamics, contributing to advancing the field of plant biomechanics.

Revue systématique sur l’utilisation des technologies en prévention des troubles des conduites alimentaires : portrait des approches actuelles et recommandations

Le projet vise à identifier les modalités technologiques (ex., application mobile, messagerie instantanée, jeux sérieux, etc.) utilisées dans les interventions existantes pour prévenir les TCA; et comparer leurs efficacités. De plus, ce projet examinera les différents publics cibles et les caractéristiques des individus ciblés par les interventions numériques existantes pour prévenir les TCA. Cela inclut, par exemple, l’âge, le sexe et le rôle (ex., parent, éducateur, enfant, etc) des individus ciblés. Nous comparerons également l’efficacité des interventions préventives selon ces différents contextes. Les retombées potentielles du projet incluent la génération des connaissances qui pourraient mener à des répercussion concrètes en TCA, où les besoins en prévention sont incontestables et une meilleure compréhension comment la technologie peut être mise à profit comme outil de prévention en TCA. De plus, nous serons également en mesure d’identifier les lacunes et d’émettre des recommandations quant aux approches optimales, en plus de clarifier si l’efficacité et la pertinence des interventions varient en fonctions des caractéristiques de leurs public cibles. Le stagiaire sera capable d’acquérir et d’améliorer ses compétences en recherche dans des domaines variés, dont la psychiatrie, la méthodologie et la technologie en développant plusiers aptitudes essentiells en recherche.

Conformal prediction, fairness and calibration

The internship focuses on the intersection of mathematics, machine learning, and ethical AI, specifically within the domains of conformal prediction, fairness, and calibration. Conformal prediction is a statistical framework that provides mathematically rigorous confidence measures for machine learning predictions, ensuring that the uncertainty quantification is valid under minimal assumptions. In this project, the goal is to explore how conformal prediction methods can be extended or adapted to meet fairness criteria, addressing biases that may arise in datasets or prediction algorithms.

Kootenay Outdoor Recreation

Textile waste accounts for 10% of global carbon emissions while millions of tonnes of jackets, clothes and backpacks end up in landfills every year. New gear manufacturing impacts the environment through production and distribution cycles, through the mining, synthesis and processing of raw materials, and through microplastic, chemical and dye discharge into the environment. But what if we could reduce that impact locally, lessen the burden on regional landfills and create an economy of sustainability? Travelling to 18 locations across the Kootenays this summer, including all the recreation hotspots, the KORE ReHub Repair and Sustainability Tour will assess and repair your outdoor gear, on site! Textile and apparel repairs that can’t be done onsite in our mobile custom repair trailer will be sent to a central location and outsourced to our network of repair specialists. Come and join us: reuse your gear, help the environment, and establish the Kootenay region as a leader in North America for outdoor gear circularity.

Analyzing process data alongside traditional item responses to obtain more accurate imputation, offering a deeper understanding of respondent behavior and enhancing the quality of imputing missing responses

This project aims to enhance proficiency estimation in large-scale assessments by improving missing-data imputation techniques. Specifically, the study focuses on refining Multiple Imputation with Denoising Autoencoders (MIDAS)—a deep learning-based approach—by incorporating item response time as an additional contextual feature. Unlike traditional item response theory (IRT) or regression-based methods, which rely on strong assumptions, the proposed approach leverages the flexibility of deep learning to better handle the complexity and dimensionality of large-scale assessment data.

AI/ML in Applied Marine Bioacoustics: Exploring the transfer of existing models from other domains

This project aims to answer the research question “can existing AI/ML models from other domains be applied to help address marine bioacoustics challenges?”

One of the key challenges is that marine bioacoustics lags behind terrestrial bioacoustics in the level of research attention and technical advancement. Additionally, bioacoustics as a field has been slower in leveraging AI/ML techniques compared to other domains, such as speech recognition and medical imaging.

The Mitacs intern will:
1. Expand the preliminary literature review undertaken during Winter 2025 through a Memorial University Professional Skills Development Program 60-hour Global Student Exchange placement.
2. Curate and clean a database of known/identified sound recordings for selected marine species, as well as recordings of marine environments containing many sounds (ambient, shipping, marine mammals, fishes, etc.).
3. Use the cleaned data to test the effectiveness of existing AI/ML models transferred from other domains.
4. Engage experts for challenge identification/confirmation, species selection, data provision and results verification.

newkid x Patricia: Stepping into the front doors as the new kid

newkid® is a creative company specializing in branding for startups that want to stand out. We establish and evolve brands to have a strong sense of self, clarity of purpose, distinctive style, and a singular perspective. Our work spans across brand strategy, creative direction, visual identity, naming, advertising, packaging, and digital experiences.

The challenge: As the demand for brand design services grows, startups expect more innovative, efficient, and dynamic branding solutions to differentiate themselves in highly competitive markets. Traditional branding processes can be resource-intensive, requiring extensive research, exploration, and iteration. Additionally, newkid aims to refine its internal design methodologies and leverage new tools to enhance efficiency while maintaining the quality and creativity we are known for.