Projets novateurs réalisés

Application for 5-axis 3D printing project at Concordia University under the supervision of Dr. Tsz Ho Kwok

We aim to develop new techniques in 5-axis Computer Numerical Control (CNC) machining and 5-axis 3D printing, which are advanced methods used in modern manufacturing. Our project focuses on creating collision detection algorithms to prevent accidents with expensive machinery, ensuring safety remains a top priority. We also plan to develop tool path planning algorithms to make manufacturing processes more precise and time-efficient—both crucial aspects in production. To achieve these goals, we’ll explore the geometric properties of the machines and the parts being made, potentially using Machine Learning methods to assist professionals in their work. This collaboration between BCAM and Concordia University is expected to benefit both institutions by fostering new partnerships for future projects and producing high-quality, impactful research publications in this field.

Study of environmental impact of activity recognition systems

This research project aims to analyze and compare the environmental impact of various architectures used in activity recognition systems, with a focus optimizing the balance between (enough) quality of service (QoS) and sustainability. Specifically, the project will focus on the data life-cycle that flows through the three
distinct architectural approaches—device-based, edge-based, and cloud-based— considering the physical devices that support them, the data transferred, the data processing algorithms, and data storage.

Sharing small spaces: Social tolerance and conservation in capuchin monkeys living in rescue centers and a forest fragment

Habitat loss and fragmentation is occurring rapidly throughout nonhuman primate ranges around the globe. Advancing knowledge on how capuchin monkeys (_Sapajus libidnosus_ and _Sapajus flavius_) respond to living in smaller spaces, particularly in relation to the role of social tolerance in feeding and foraging, will provide critical information on the adaptive capacity and resilience of these species in the face of environmental change, and help shape conservation strategies for them and the endangered ecosystems they inhabit. This proposed Globalink Project would create international research experiences for five young scholars, conducting field work in northeastern Brazil with analysis and science communication training in Canada. The study sites include a fragment of the Atlantic Forest, while the research on the captive capuchins will take place at wildlife rescue centers in Brazil. This project will build on existing connections to help establish a solid, long-term collaboration between supervisors and research groups. This project will contribute to advanced degree requirements for all student participants, and we also expect to produce 3-4 scientific publications and 5 conference presentations to share our findings and help spread conservation knowledge and techniques internationally.

A Bot for Patch Linkage Detection In Modern Code Review Platforms

Code review tools are essential in software quality assurance for projects like Android and OpenStack, and companies such as Google and Microsoft. OpenStack alone has over 100,000 contributors and 600+ repositories, where parallel patch submissions often lead to redundant work. Studies indicate that identifying duplicate patches takes, on average, 2.5 reviewers and 5.2 comments per review, delaying processes and raising maintenance costs.

Detecting and linking patches early helps highlight dependencies, broader context, or alternative solutions, reducing redundant efforts and streamlining reviews. This capstone project aims to develop a tool that automates the detection of patch linkages using advanced machine learning techniques. It will evaluate the performance of large language models (LLMs), pre-trained transformers, and basic ML models in detecting patch linkages in real-world open-source projects like OpenStack and Eclipse, which use Gerrit for code review.

Advancing NLP-based Techniques and Quantitative Models at Picton Mahoney Asset Management

Picton Mahoney Asset Management (“PMAM”) was founded in 2004 to provide unique investment solutions to institutional, retail and high net worth investors in Canada and around the world. They are 100% employee-owned and manage approximately $10.8 billion in sub-advisory, pension plan and hedge fund assets on behalf of their clients.

The Quantitative Research and Risk team at PMAM is dedicated to developing and maintaining models that support investment decisions and risk assessments. However, with the evolving complexity of financial markets and the increasing demand for data-driven insights, the team faces challenges in optimizing model accuracy, enhancing operational efficiency and improving the predictive power of risk assessment tools.

By advancing NLP-based techniques and quantitative models, this project aims to address these specific stakeholder needs, enabling the team to generate actionable insights from both structured and unstructured data, and improve the precision and robustness of predictive analytics, offering a competitive edge in client portfolio management and risk mitigation.

This project requires expertise in NLP, machine learning, statistical analysis and finance. Familiarity with R, Python, SQL and machine learning libraries, like TensorFlow or PyTorch, will enable effective model development and backtesting. Knowledge of financial risk modeling and familiarity with specialized tools for portfolio management and sentiment analysis is essential.

University of Toronto’s Master of Mathematical Finance (MMF) students bring technical and financial expertise that meets PMAM’s Quantitative Research & Risk team’s needs. They’ll contribute advanced programming skills in R and Python to PMAM’s quantitative investment platform by researching and maintaining alpha-generating strategies. Their hands-on experience in quantitative analysis and model development enable them to enhance PMAM’s model accuracy, operational efficiency and predictive power.

Through this internship opportunity, PMAM can support their transition from academia to industry while benefiting from their fresh perspectives, technical innovation and data-driven methodologies to strengthen PMAM’s quantitative investment strategies.

The effect of using roller massager on cortical and spinal excitability

Myofascial pain syndrome can lead to chronic pain. With fatigue or trauma, muscle spindle reflexes activate spinal motoneurones causing them to fire constantly leading to spasm. These reflex signals can also be transmitted to the brain as well. It could be hypothesized that if massage therapy (roller massager included) could regulate cortical and spinal motoneurone excitability, it may also decrease unnecessary reflex activity. Previous investigations from our lab demonstrated that applying massage over the musculotendinous junction could diminish the motoneurone excitability. This procedure may help to decrease pain. The aim of our study is to assess the effect of using roller massager on spinal and cortical motoneurone and muscle excitability. Hopefully by identifying precisely how the tool works we can suggest the most effective application and procedures.

Elucidating the role of the peripheral-brain immune axis in Parkinson’s disease

Parkinson’s disease is commonly described as a brain disorder, but recent research suggests that early events outside the brain (periphery) may influence Parkinson’s disease neuropathology. The blood-brain barrier is a structure that protects the vulnerable brain tissue from blood-borne toxins and circulating immune cells, but barrier function appears compromised in PD. A consequence, there may be increased entry of toxic peripheral elements and immune cells intro the brain, and these peripheral factors could reach brain cells and possibly accelerate disease progression. Astrocytes, crucial for BBB maintenance, become impaired in Parkinson’s disease, further weakening this barrier. A new 3D brain-chip model built using induced pluripotent stem cells enables detailed study of these interactions. This model allows researchers to observe how astrocytes and peripheral immune cells contribute to blood-brain barrier dysfunction, and the subsequent entry of potentially toxic elements into the brain. The proposed research will help define the role of the brain-periphery immune axis, which could uncover new targets to slow Parkinson’s disease progression, offering hope for disease-modifying therapies.

Development and Validation of a Deep Learning-Based Infrared Thermography Model for Detection and Differentiation of Thyroid Nodules

Patients referred for thyroid nodule (TN) assessment will be invited to participate in the study. Patients will undergo screening using infrared thermography (IT) and ultrasound (US) with needle insertion. Neck IT thermograms will be captured using the FLIR TS865 for all patients’ frontal and lateral norms. With the IT camera software, the regions of interest (ROI) will be defined at the thyroid glands. The reference examination will label the IT images according to the following classifications: 1) control, 2) TN, and 3) TN cancer group. The IT images will undergo enhancement and segmentation using Active Contour without Edge (ACWE). The segmented images will build a convolutional neural network (CNN) that utilizes features extracted from the acquired IT images for detection and differentiation. The CNN will then be tested and validated for its use as the first screening tool for thyroid nodules.

Nature-derived solutions: Development of a West African herb-derived compound for tackling infections

More than 25 million Africans and 800,000 Canadians suffer from chronic wounds. Wound infections are a leading cause of hospitalization and disability in immunocompromised people and vulnerable populations. This is compounded by the indiscriminate use of antibiotics, leading to antimicrobial resistance and failure of treatments. Therefore, antibiotic alternatives are needed. Inspired by traditional wisdom, phytotherapy (plant-derived compounds) may unlock the potential to develop therapies that can simultaneously eradicate infection and promote wound healing. Supported by the MITACS Globalink Research Award, the intern will investigate an alkaloid commonly used by the Indigenous peoples of Africa for treating infectious diseases. The intern will receive hands-on experience in characterizing the effects of this alkaloid against complex biofilms in wound microenvironments. He will gain expertise to apply confocal microscopy to interrogate the spatiotemporal architecture and composition of biofilms. He will also develop mechanistic assays to identify the potential antibiofilm mechanisms of this alkaloid. This project will capitalize on the expertise of the two teams in natural products research and microbiology to propel the development of a novel agent for treating wound infections.

Numerical Optimization of Solar Photovoltaic Thermal System Performance and Reliability under Extreme Climate Conditions

Renewable energy based on solar photovoltaic thermal (PV/T) systems is gaining significant attention as an alternative energy source to reduce the global carbon footprint. However, the performance and reliability of these systems can decrease considerably under extreme climatic conditions, such as high and low temperatures and fluctuating humidity levels. Despite this, our understanding of the effects of various controlling parameters and the ability to model and predict thermomechanical degradation based on reliable experimental data require improvements. This project will use computational fluid dynamics (CFD) and finite element analysis (FEA) to perform numerical modeling of PV/T systems, assessing and optimizing their reliability under varying extreme climate conditions. The insights gained from this work have the potential to guide the development of more reliable PV/T modules suitable for both temperate and tropical climates. These efforts will contribute to the advancement of several United Nations Sustainable Development Goals.

Urban Sanctuary and Migrant Solidarity in South Africa and Canada

Cities are key sites for understanding migrant integration and refugee protection around the world. In fact, many cities protect and include inhabitants who are denied legal status by nation states. In Canada and the US, these cities are called “sanctuary cities;” in other countries they have labels such as solidarity cities, cities of refuge, communities of reception, and cities of welcome. Although similar urban initiatives exist in Africa, there has been no coordinated cross-continental comparison of local policies and practices. This Mitacs Globalink Research Award project aims to bridge this gap in the literature through a comparative study of local migrant solidarity practices in Canada and South Africa. The proposed research explores the convivial encounters experienced by migrants across formal and informal urban spaces in Canada and South Africa. In focusing on the informal economy, community-based housing organizations, and transnational actors, this research aims to shed light on how solidarity emerges in the absence state institutions support precarious migrants. This Mitacs Globalink Research project will harness and develop international exchange, and facilitate knowledge mobilization and policy transfer between the Global North and the Global South, where novel approaches to migration and refugee accommodation are explored, tested, and practiced.

Design and Seismic Performance of Steel CBFs: Advancing Standards through Canadian and European Code Comparison

This research project aims to investigate the seismic behaviour of steel concentrically braced frames (CBFs), a widely used seismic-resistant system in Canada and Europe. Previous experimental and numerical studies have identified several shortcomings regarding the seismic performance of steel CBFs. In response, this project seeks to propose new design guidelines within the framework of the Canadian and European seismic codes to improve the behaviour of steel CBFs under seismic loads, aiming to promote global ductile mechanisms and prevent drift and damage concentration under earthquakes. The project findings will contribute to advancing seismic design methods for steel-braced frame structures, improving current structural design practices, and strengthening the resilience of communities in seismic-prone areas. Such enhanced provisions are expected to potentially reduce structural damage and economic losses during major earthquakes and contribute to saving lives by ensuring safer structures. Additionally, the outcomes of this research will support the construction industry in achieving more cost-effective and optimised designs without compromising safety. The data generated in this project will also serve as a valuable resource for future research initiatives, further enlarging knowledge in the field of earthquake engineering and contributing to the state of the art of steel CBFs.