L2M – BIM-Enabled Multi-Criteria Decision-Making: Towards Efficient Building Element Selection

The project focuses on creating a new software tool that combines multi-criteria decision-making (MCDM) methods with BIM, a 3D digital modeling system used in construction. This tool will assist architects, engineers, and builders in selecting materials and elements by evaluating multiple criteria, such as cost, durability, and their environmental impact. By addressing challenges like too […]

Read More
L2M – BioinformHER

Bioinformatics is fundamental to modern medicine, driving breakthroughs in cancer, pandemics, and disease understanding, however, while demand for this field continues to grow, its workforce still does not reflect the people it serves. Fewer than 20% of bioinformaticians are women, and less than 5.7% are Black [1]. This homogeneity reflects and perpetuates systemic biases across […]

Read More
L2M –SynapPlay

This project aims to help older adults and people with neurological conditions such as stroke or Parkinson’s disease stay independent longer by developing a tablet-based digital therapeutic tool called SynapPlay. The platform combines physical and cognitive tasks with real-time feedback to improve balance, thinking skills, and motor control, all from the comfort of home. Through […]

Read More
L2M – Augmented Reality-Enhanced Precision Robotic Manipulator Simulation and Training System

The global manufacturing sector is shifting towards high-mix, low-volume (HMLV) production. While large corporations can invest in advanced automation, Canada’s small and medium-sized enterprises (SMEs) face a significant “adoption chasm.” This gap is driven by prohibitive upfront capital costs for robotic systems, a critical lack of in-house programming and maintenance expertise, and the financial risk […]

Read More
L2M- AI Lab Assistant

This project initially targets drug discovery labs, with long-term plans to expand into broader biomedical research. As AI becomes integral to scientific work, we aim to build a custom AI assistant that goes beyond basic chat support, capable of running code, executing ML/DL pipelines, and assisting with lab-specific tasks.

Read More
L2M – U-Pro Soccer

U-Pro Soccer is a Canadian sportstech company developing a mobile-first training platform that uses AI and computer vision to help youth athletes practice soccer drills at home using only a smartphone and a training mat. Many young players lack access to structured training, especially outside of team environments, and U-Pro aims to fill this gap […]

Read More
L2M – Validating Nanobubble Technology for Sustainable Water Use in the Canadian Ecosystem

This project focuses on exploring how a new water based technology—called a nanobubble generator—can help solve common challenges in agriculture, aquaculture, and water treatment across Canada. Nanobubbles are minuscule gas bubbles that improve water quality by increasing oxygen levels, cleaning surfaces, and reducing the need for harmful chemicals. While this technology has seen success in […]

Read More
L2M –Sustainable alternative supplementary cementitious materials using oat husk

This project explores innovative ways to convert agricultural residues (i.e., oat husks) into sustainable construction materials. Specifically, the research focuses on developing and validating cement replacement products derived from agro-waste, which can help reduce the carbon footprint of concrete, a major contributor to global greenhouse gas emissions. The intern will work closely with both academic […]

Read More
Early Detection of Grain Spoilage and Contamination Using Hybrid NIR-E-Nose Systems with Machine Learning

This project explores the feasibility of using a hybrid sensing system that combines Near-Infrared Spectroscopy (NIR) and Electronic Nose (E-Nose) technologies, enhanced by machine learning, to detect early signs of spoilage and contamination in stored grains. By analyzing changes in grain structure and gas emissions, the study contributes to ongoing research on reliable, non-destructive, and […]

Read More
Forecasting Levodopa-Induced Dyskinesia in Human Subjects with Parkinson’s Disease

We have developed a novel data augmentation procedure that significantly enhances machine learning-based classification of different brain imaging scans. Having successfully demonstrated proof-of-concept in a rodent model, we are now expanding this approach to clinical applications in humans. Specifically, we aim to utilize this technology to identify early biomarkers of neurodegenerative diseases, enabling personalized treatment […]

Read More
L2M – GradFinder

We are building GradFinder, an online platform that connects students with professors who are actively recruiting for thesis-based graduate programs. Many students struggle to find available positions, and professors are overwhelmed by unqualified inquiries. Our platform streamlines this process, improving access and efficiency on both sides. Through this project, we aim to refine our business […]

Read More