Innovative Projects Realized

L2M- Lightweight Additive Manufacturing of Marine Components for Fuel Efficient and Low-Emission Shipping

The proposed project aims to develop advanced materials that can better resist corrosion and damage in marine and offshore environments. These materials will help extend the service life of equipment and structures exposed to harsh seawater conditions, reducing maintenance costs and downtime. By improving durability and reliability, the project will support safer and more efficient operations for the partner organization and contribute to more sustainable marine infrastructure management.

The Paternity Crisis and the Other Side of Turkish Modernity

My research project presents a unique perspective on the Turkish experience of modernity from the side of the discredited, disavowed and repressed, and thus contributes to a better understanding (and to a better identification of the failures) of the modernization in Turkey that predominated much of the twentieth century. It does this through an exploration of paternity crisis that hinges upon the traumatic inception of modernity. The killing of the father lies at the foundation of law which substitutes the abstract principles of equality, liberty, and fraternity for paternal/traditional authority—that resonates with the modern state born of the French Revolution.

L2M – Novel Cryo-Microfluidic Device for Cell Cryopreservation

Developing a novel cryo-microfluidic device for ultra-rapid, cryoprotectant-free cell preservation. The project advances prototype design, testing, and market validation to improve biotechnology and regenerative medicine applications in Canada.

L2M-Digitalizing On-Demand Inventory with AddManuChain Platform: An Innovative Bridge between AI and Additive Manufacturing for Sustainable Industrial Operations

The project aims to prove and commercialize AddManuChain, a computerized inventory system that will deploy on-site 3D printing to print certified parts on location at the remote industrial sites, including offshore oil platforms, mining operations, and also in the maritime facility to eliminate the expensive effect of long downtimes incurred during part replacement. The project will build a strong business case to replace traditional rush-freight logistics with localized production capabilities that meet high standards in the industry by specific measures such as developing prototype workflows that reflect on certification requirements, conducting thorough stakeholder interviews, and undertaking intensive economic modelling. The new innovation is based on the established Canadian leadership in high-order manufacturing, thus making the country a leader in terms of the digital transformation of the industrial supply chains. The established research and development base, manufacturing experience, and continuous emphasis on innovation are the attributes of Canada that are the best foot forward in this innovation. With an advantage in the additive manufacturing and Industry 4.0 technologies, the project will also strengthen the reputation of Canada as a world manufacturing leader. The expected results are the increased resilience in the Canadian resource base, increased domestic advanced manufacturing capacity, significant benefits to the environment through less logistics emissions as well as competitive edge that Canada would get in the international market through innovative next-generation smart manufacturing solutions. As a result, this study has placed Canada at the forefront of trans formative design, production, and distribution of essential elements, thus creating new prospects to the Canadian business on both fronts, as well as mitigating viable challenges faced by remote processes of industrial activities.

L2M – VirtualSLP

VirtualSLP is an AI-powered platform transforming care for patients with neurologic speech disorders. Conditions such as ALS often cause bulbar impairments—loss of speech, swallowing, and facial expression—that devastate quality of life. Yet clinicians, particularly speech-language pathologists (SLPs), face overwhelming barriers: caseloads as high as 600 patients per year, only 15–20 minutes per visit, and no standardized, easy-to-use tools. As a result, care is inconsistent, delayed, and less effective than it could be.

The need for better solutions is clear. Our team, led by Dr. Yana Yunusova, has decades of experience at the intersection of research and clinical practice. Communities of Practice uniting 200+ SLPs in Canada and the U.S. further highlight the urgency and scale of the problem.

VirtualSLP addresses this gap with a web-based platform that integrates remote multimodal assessments and AI-driven insights. Patients complete validated assessments online, while the platform extracts 200+ acoustic and movement features to detect early signs of bulbar disease and classify severity. Upcoming modules will predict disease progression within a six-month window and deliver GenAI-powered, evidence-based recommendations, enabling personalized, efficient, and scalable care.

Backed by $3M in research funding, VirtualSLP is clinically validated and built with direct input from both patients and providers. With a $400M obtainable market and 70% of SLPs projected to adopt telehealth by 2030, it is positioned to become a game-changing solution that improves patient outcomes, streamlines clinician workflows, and scales across healthcare systems.

VirtualSLP: where AI meets patient-first neurological care.

GOWL-Edit, un assistant logiciel pour l’édition graphique d’ontologies OWL 2, de requêtes SPARQL et de règles SWRL (Semantic Web Rules Language) dans la syntaxe G-OWL étendue

L’Institut de Recherche en Électricité du Québec (IREQ), qui est le centre de recherche d’Hydro-Québec, a pris le virage des technologies du web sémantique depuis quelques années. L’IREQ doit gérer une quantité énorme d’informations provenant de ses équipements réparties dans tous le Québec. Les chercheurs de l’IREQ ont notamment choisis les technologies du web sémantique afin de faciliter la cueillette et la gestion des informations. Au coeur des technologies de web sémantique réside une pile langagière qui permet de textuellement coder les informations et la sémantique qui s’y rattache. Les chercheurs de l’IREQ souhaiteraient développer une solution afin de faciliter le processus de modélisation de la sémantique des données par la conception d’un langage graphique (G-OWL étendu) et d’un assistant logiciel de modélisation graphique (GOWL-Edit). Techniquement, GOWL-Edit sera en mesure d’assumer l’édition de fichiers dans le format : de l’Ontology Web Language version 2 (OWL 2), du langage de requêtes SPARQL et du langage de règles Semantic Web Rules Language(SWRL). Comme point de départ de la solution, il est envisagé d’exploiter le G-OWL, une syntaxe graphique de l’OWL 2 issus du résultat de l’une de nos recherches antérieures.

Developing an AI-Driven Personalization Framework for Menstrual and Chronic Pain Management

This project lays the foundation for the next stage of development of Juno Technologies’ medical and connected device: integrating artificial intelligence (AI) to provide personalized, adaptive menstrual pain management.

L2M – Visum Neurotechnologies

The project aims to develop a non-invasive, dual-modality tool combining eye-tracking (ET) and functional near-infrared spectroscopy (fNIRS) to measure cognitive capacity and enable early detection of cognitive impairments in older adults. Using short-term memory tasks, it captures objective biomarkers (e.g., pupil size and prefrontal neural activity) to outperform the conventional tests in sensitivity and cultural neutrality.

L2M Novel Hybrid Scanless Confocal Fluorescence Microscopy System

Our project introduces a novel hybrid fluorescence microscopy system that merges the strengths of light-sheet and confocal microscopy while eliminating their key limitations. The system is fully scanless, removing the need for any motorized scanning devices—resulting in faster, simpler, and more reliable imaging. It also integrates a signal intensifier to detect weak fluorescence and a microfluidic channel for automated, high-throughput sample handling.

From an academic perspective, this technology enables rapid, high-resolution, and low-phototoxic imaging, ideal for studying cancer cells, microorganisms, and developing tissues. The combination of scanless confocality and light-sheet illumination allows continuous, non-invasive observation of cell behavior, division, and drug response in real time, even in samples with weak fluorescence signals.

From a business perspective, the platform offers a compact, affordable, and user-friendly alternative to existing expensive and complex microscopes. By combining speed, resolution, and automation in one system, it reduces costs, training time, and maintenance—providing strong return on investment for research and clinical users. With the global fluorescence imaging market expected to grow from USD 11 B to 20 B by 2030, this hybrid scanless microscopy system stands as both a scientific innovation and a high-potential commercial product for research, diagnostics, and drug discovery.

Preclinical characterization of medical marijuana cultivars with therapeutic efficacy and low side-effect liability

Medical marijuana describes the legal use of cannabis plants for treating health-related issues. In Canada, there are currently about 40,000 medical marijuana users, and this number is expected to increase by over 1000% during the next decade. The large majority of patients use medical marijuana for its psychological effects, such as for anxiety, depression, pain and insomnia. There is a concerning lack of knowledge about the effects of different strains (“cultivars”) of medical marijuana, including both therapeutic effects as well as unwanted side-effects. In the present application, we propose to study different cultivars for their therapeutic potential and side-effect liability using preclinical protocols. Working with our Industry partner, we will select their most promising cultivars and test them in a comprehensive behavioral test battery, using state-of-the-art animal models. Cultivars will be extensively screened for anxiolytic and antidepressant properties, while sedating and cognitive side-effects will also be evaluated. The results of these studies are expected to result in the identification of specific cultivars that can be used as promising candidates for downstream clinical trials involving the human patient population and who are often refractory to existing pharmacotherapies

L2M Highly Efficient DC-DC Converters for Small Offshore Oil Rigs

Offshore oil rigs play a critical role in Canada’s energy industry, but they rely heavily on diesel generators that are expensive, polluting, and difficult to maintain in remote marine environments. This project explores the development of highly efficient DC–DC converters—advanced electronic devices that improve the way electricity is managed on offshore platforms. These converters make it possible to integrate renewable energy sources, such as solar and wind, with existing systems, helping to reduce fuel costs and greenhouse gas emissions.
The goal of this project is to design, test, and evaluate a converter prototype optimized for small offshore oil rigs. By focusing on efficiency, compact size, and durability in harsh marine conditions, the project will provide a practical solution to improve energy reliability and sustainability. The outcomes will benefit Canada by supporting cleaner energy use in offshore operations, reducing environmental impact, and advancing technologies that can also be applied to other remote and marine industries.

L2M: AI-assisted marine ecosystem monitoring

This project aims to turn raw underwater video into decision-ready science. Our software automatically detects, classifies, and counts marine species, then delivers results in a simple web dashboard with linked video clips, maps, and exportable reports. Instead of annotating from scratch, ecologists just review and correct AI suggestions—cutting review time by more than half. Built for real-world conditions, it flags unknown species, handles uncertain IDs with genus- or family-level counts, and works across different cameras, habitats, and seasons. Backed by peer-reviewed methods and expert-annotated Atlantic datasets, and led by my PhD research in partnership with Fisheries and Oceans Canada, this solution combines scientific rigor with deployable AI. Whether regulators, operators, or researchers, our users can ask: “How many cod were at Site 12 in June?” and get traceable, audit-ready answers in minutes instead of months.