Innovative Projects Realized

Process flow improvement at a Parallam mill using simulation modeling

In this project, the intern will study the production process at a Parallam mill in Vancouver working closely with its Production Manager. The objective of this work is to develop simulation models to evaluate potential alternatives in order to improve the process efficiency of the final stage of operations at the mill. The final stage is called the remanufacturing department. This stage is the bottleneck of the system and limits the mill’s productivity. It is operational 4.5 days a week and the intention is to be able to have it operational 24/7. It is important to first study the system to identify issues limiting the throughput of the remanufacturing department and propose potential scenarios for improvement. Then, simulation models will be developed to evaluate each potential scenario and determine the best option to help the mill increase its outputs

L2M – Low-Pressure-Drop Air Filtration for High-Volume HVAC Systems

Wildfire smoke and airborne pollutants are an increasing threat to indoor air quality, yet existing filtration systems struggle to balance efficiency, energy use, and proper airflow in large-scale applications. This venture is developing a novel air filtration system that integrates directly into building air handling units to deliver superior particulate removal without high pressure drop or energy consumption. By leveraging advanced particle flow dynamics, this technology can provide adaptable, low-maintenance filtration for large indoor spaces such as sports facilities and commercial spaces, as well as critical environments including hospitals and manufacturing facilities. In a world facing climate-driven air pollution challenges, this technology addresses the growing need for resilient, energy-efficient air quality solutions.

Innovative 3D-Printed Hip Protection System

This research project aims to develop a next-generation hip protector insert for hip protector pants that is more hygienic, discreet, and wearable than current market options. Traditional foam-based insert/protectors are often bulky, uncomfortable, and visually unappealing; factors that reduce user compliance, especially among older cognitive adults. Our goal is to find a design for a pant insert that’s slim, low-profile and uses advanced 3D printing (FDM) with flexible materials, such as TPU, to create a lightweight structure that better manages impact forces from falls. Unlike conventional solutions, our design focuses equally on performance, comfort, and aesthetics, key factors for encouraging everyday use. The insert will undergo rigorous impact testing (drop tower) to validate its effectiveness under real-world fall conditions. Consistency and quality in manufacturing will be central, ensuring a scalable, high-performing product. For the partner organization, this project presents an opportunity to bring a truly innovative product to market, one that responds to a critical need with a modern solution. By combining biomechanical insight with additive manufacturing, we aim to position this product as a leading option in personal fall protection, ultimately supporting injury prevention, healthcare cost reduction, and improved quality of life for vulnerable populations.

L2M-Commercialization of a Multiscale Computational Modeling Platform for Cellular Organelles and Membrane-Protein Systems

This project aims to commercialize a computational biotechnology platform that simulates the structure and dynamics of cellular organelles, such as the endoplasmic reticulum, at multiple scales. These systems play key roles in many cellular processes, and their malfunction is linked to diseases such as Alzheimer’s, ALS, and cancer. While experimental methods provide valuable insights, they are often limited in capturing how molecular-scale changes translate into large-scale structural outcomes.

The proposed innovation addresses this gap by transforming an advanced academic simulation framework into an industry-ready software tool. Through structured market discovery, customer validation, and business model development, the project will identify real-world applications for this technology in pharmaceutical research, synthetic biology, and biomaterials design. The intern will work closely with industry and academic mentors through the Lab2Market Launch program to refine commercialization pathways and define a sustainable go-to-market strategy.

By bridging the gap between academic research and industrial application, this project will contribute to Canada’s innovation ecosystem—accelerating research translation, supporting biotechnology development, and fostering new opportunities for scientific and economic growth.

Modèle de prévision dynamique du comportement des renouvellements de polices d’assurance

Le département de Prévision et Analytique de TD Assurance est actuellement en charge de prédire les ventes et les revenues des différents produits d’assurance sur une base annuelle et mensuelle. Prédire les revenues, et ceci de manière précise, est crucial pour l’entreprise et son bon fonctionnement. Les modèles jusqu’alors utilisés ont étés construits il y a un peu plus de dix ans et ont été implémentés avec le logiciel Excel. Ces modèles sont complexes et requièrent trop de manipulations manuelles, engendrant non seulement des erreurs potentielles mais également une perte de temps. Le but du présent projet est de développer de nouveaux modèles, tout aussi performant, sinon plus, de les automatiser et de les dynamiser, notamment grâce au logiciel SAS. Nous nous concentrons sur les modèles de renouvellements et d’annulation de polices d’assurance afin de généraliser le phénomène à l’ensemble des produits d’assurance. Ces deux modèles sont dynamiques car directement liés l’un à l’autre. L’utilisation de séries chronologiques est ici requise pour construire les dits modèles.

L2M – AI Chat Agent for Proposal Generation and Knowledge Reuse

This project is creating a smart chat assistant that uses Artificial Intelligence (AI) to help construction and engineering companies write accurate proposals, like bids for major jobs. The assistant is trained on the company’s successful past proposals, making it a secure, private expert. When a team uploads a new Request for Proposal (RFP), the assistant instantly finds and customizes the best text from their past work to match the new job’s requirements, helping them avoid repetitive, manual copying and pasting. The huge benefit for the partner organization is that they can save hundreds of hours of staff time on proposal writing, make sure all their proposals are high-quality and consistent, and ultimately win more contracts by speeding up the process and improving the final submission.

Machine Learning developer intern working within cross-functional teams to develop and commercialize AI-powered solutions in the Public Services sector – AltaML

The project comprises internships in a variety of technical and business roles within our organization and within our GovLab program. Within the organization, roles include associate machine learning developer, business development associate, communications associate and finance associate. Within GovLab specifically, roles include associate machine learning developer, associate business solutions consultant, and project delivery associate. The diff erence is mainly that in GovLab, there is a focus on public sector problems, whereas in AltaML overall, we work across sectors.
For the Machine Learning Developer intern listed on this particular application, Seyed Arshia Razavi (Arshia) will be working with the Government of Alberta – Primary and Preventative Health Services team as they look to pilot their use case (‘IHDA Public Health Data Navigator’). Albertans are challenged to know what data is available and the specifics of what they are looking for before starting their search, which results in significant eff ort for finding information and and lack of transparency and accessibility to public health information. The project aims to solve this by creating a conversational assistant that makes structured knowledge more accessible without requiring users to know exact dataset details.
? Value: the value of this project is multi-faceted, significantly benefiting the general public and Health Analysts. For the general public, the value is in improved accessibility and transparency of public health information, making it easier to find and retrieve data without prior knowledge. The benefit will be estimated by tracking site analytics, aiming for an increase in the number of visitors, a longer average session duration, and an increase in positive media mentions. For internal staff , particularly Health Analysts, the project is expected to decrease the staff analyst time spent generating insights from the data, reducing the current manual eff ort required for comparing and generating insights and analysis tools.
? Focus: the focus of the project is to apply machine learning, agentic workflows and natural language processing to change the way the public accesses health information.

L2M –An energy efficient technology for enhancing indoor air quality

This project focuses on commercializing a novel heat-based air disinfection technology. It aims to validate market needs, assess customer segments, and develop a viable business model to bring an energy-efficient purification solution to market, improving public health and making air disinfection affordable.

Designing a culturally inclusive information system for Canadian universities to enhance international student recruitment

The number of Asian students getting their post-secondary education abroad has doubled over the past decade to around 2.5 million. In the same period, the number of agents in the admission business in China has increased ten times to nearly 400. High school students in China find it difficult to make informed decisions independently about application and admission to foreign universities because the western university websites and information sessions are not designed keeping their cultural practices in mind. This research project undertakes an iterative, user-centered design of an online, real time information session and related international student information web pages to make them culturally inclusive of prospective students from China. The benefits from such a system for the students could include higher efficiency and reduced time for information seeking and bypassing of third party agencies. The anticipated benefit to Canadian universities would be facilitation of international student recruitment and increase in the number of international student admissions.

L2M – Vision-Based Upper-Limb Assistive Devices

Upper-limb assistive devices remain hard to use because control often feels unnatural and tiring, which leads to abandonment. The intern’s M.Sc. project proposes a non-invasive, vision-based control layer that links what the user looks at to objects in the real-world 3D environment and predicts natural hand paths and joint actions. This approach is intended to lower mental effort, shorten training, and support embodiment so devices feel more like part of the body. Canadian prosthetic and exoskeleton teams can use the results to choose control features, define clear performance targets, and plan integration with existing hardware and software. Through Lab2Market, industry and clinical stakeholders will be engaged to identify adoption barriers, ethics and workflow needs, and pilot designs, creating a practical path to a validated, commercially viable product in Canada’s digital rehabilitation space.

Intrafibrillar vs surface mineralization in bovine flexor tendon-derived collagen fibrils – an in vitro model of calcific tendinopathy of the human Achilles.

Calcific tendinopathy (CT) is a staged, reactive process where pre-calcific changes are followed by a calcific phase (formative, resting, resorptive) and by post-calcific remodelling in which hydroxyapatite-like deposits form and quite often resorb spontaneously. CT is most prevalent in the neck, foot and hip and within the foot. The Achilles is a well-documented site of CT, yet in all cases clinical imaging cannot resolve where mineral reside relative to collagen fibrils (intrafibrillar vs surface/extrafibrillar) which is essential to understand the impact of CT on tendons’ mechanical properties. To better understand mineral deposition in CT, we propose to develop an in vitro model that interrogates a formative-phase-like, acellular window at 37 °C using tendon-derived collagen fibrils from bovine SDF to determine mineral location and its fibrillar-scale nanomechanical signature under controlled conditions. In addition, we will take advantage of a recently published method to locally destabilize collagen molecular packing within single fibrils to test whether mechanically damage is required to nucleate the formative phase in CT.

Development and Validation of the Next GenerationSimulation Platform for High-Rise Buildings

Recent years have witnessed a boom in the construction of modern high-rise buildings in megacities around the world. It is important to design a high-rise building that can effectively withstand both wind and earthquake loads. Nonetheless, in current practice, the design of high-rise buildings for wind and earthquakes is done independently. Hence, there is an urgent need to develop a set of integrated design guidelines for both wind and earthquake loads. The proposed research is focused on the development of tools and methods for efficient and reliable simulation, which will lead to an analysis platform for the understanding of the response of high-rise buildings, subject to both wind and seismic loads. Kinetica is a leader in the design of tall buildings and the simulation tools developed during this project will create working platforms for Kinetica to advance its competitiveness and understanding of tall building design