Innovative Projects Realized

Implementing a Computer Vision-based Collision Avoidance System for a Collaborative Surgical Assistant Robot

This project aims to enhance the safety, precision, and efficiency of robotic surgical assistants — collaborative robots designed to work alongside surgeons — by developing a computer vision-based system that prevents collisions in real time. Using cameras and advanced image processing techniques, the system will monitor the operating room, detecting people and other obstacles. It will then use an adaptive decision-making to adjust the movements of the robot based on sensory information. By reducing risks for both patients and medical staff, this technology will make robotic assistance more reliable, reducing the physical strain on surgeons. In the long term, this will allow surgical teams to operate more efficiently, reducing the number of personnel needed per procedure, and thus improving accessibility to advanced surgeries in underserved regions. Participating institutions benefit by advancing their research in artificial intelligence, robotics, and healthcare technology while fostering interdisciplinary collaboration. This project will also contribute to the development of new safety policies for collaborative robots, bridging the gap between computer vision research and surgical robot safety, and paving the way for more intelligent, adaptive, and safer robotic assistants.

Enhancing light-matter interactions with intercalated transition metal dichalcogenides

This research project pioneers the development of novel molybdenum disulfide (MoS2)/copper hybrid materials through electrochemical intercalation and exfoliation techniques. By transforming readily available powdered molybdenite—a byproduct of Canadian mining operations—into atomically thin layers with enhanced properties, the work creates a sustainable pathway for producing high-performance two-dimensional materials without organic additives that typically compromise conductivity. The integration of copper introduces unique plasmonic resonances that dramatically enhance light-matter interactions in the near-infrared spectrum, opening the possibility for advanced photodetectors with exceptional responsivity. This collaboration benefits the German host institution by advancing cutting-edge nanomaterial fabrication methods and quantum material design, while providing the Canadian home institution with an innovative approach to add value to domestic natural resources. The project’s focus on scalable production methods bridges fundamental quantum materials science with practical industrial applications, potentially advancing fields ranging from quantum sensing to night-vision technologies, while strengthening both countries’ positions in sustainable advanced materials development.

L2M – Commercial Validation and MVP Development of a Pediatric Biofeedback Tool for Grip and Pinch Strength Assessment

Pediatric therapists struggle to obtain accurate grip strength measurements because current tools were originally designed for adults. These tools require excessive hand strength and fail to engage children, leading to unreliable data. As a result, therapists often resort to subjective estimates, making it difficult to track progress, tailor treatment plans, or justify therapy to insurance providers. Our solution is a child-friendly grip strength tool with sensory feedback (lights and sounds) to make assessments engaging and ensure precise, reliable data.

Through the L2M Internship, we will focus on three key areas: IP & regulatory groundwork, business strategy, and product development. First, we will file a provisional patent and prepare regulatory pre-submissions to navigate compliance for our Class II medical device. Second, we will refine our go-to-market strategy by assessing distribution channels, reimbursement pathways, and commercialization partners. Third, we will test and iterate our prototype with pediatric therapists and children, gathering feedback to refine our design and ensure usability. Our goal is to finalize our Minimum Viable Product (MVP) by Q3 2025.

By the end of the internship, we will be positioned for early 2026 fundraising to support batch manufacturing and market launch, assuming we meet regulatory and IP milestones. This project will lay the foundation for bringing an innovative, evidence-based grip strength assessment tool to market, ensuring pediatric therapists have the data they need to make informed treatment decisions while making therapy more enjoyable for children.

Life Hacks and Gadgets project

Over six million people living in Canada have arthritis, making it one of the most common chronic conditions. The condition significantly impacts activities of daily living, workplace participation, and it is the leading cause of disability. Living with arthritis leads to pain and physical impairments and people with arthritis can self-identify as living with a disability. When impairments are combined with the lack of accessibility, people with arthritis must adapt activities of daily living and other tasks. Over 70% of persons with disabilities reported that they had experienced 1 or more of the 27 types of barriers to accessibility. Often, assistive devices (ADs) are used as well as simple and practical tips (“life hacks”) to address these limitations and barriers. To address these challenges, the Canadian Arthritis Patient Alliance (CAPA) has worked with five Occupational Therapy students from McMaster University’s Department of Rehabilitation since 2023. These students have conducted a literature identify evidence about assistive devices and arthritis, conducted a survey about the use of assistive devices in daily life, completed interviews with people with arthritis where they shared how they live life well with arthritis and a Photovoice project to bring community knowledge further to the forefront by identifying innovative adaptive strategies or devices, personal accommodations, and coping mechanisms that help people with arthritis live independently. The Mitacs funding will enable us to synthesize all of the available evidence in a way that is useful for patient decision making and design an appropriate website or other layout to communicate the evidence to the patient community.

System level defenses for Enterprise AI Agents

(1) the main activities of the partner
ServiceNow develops a platform for client organizations to manage and automate large-scale processes across various industries. In 2020, ServiceNow acquired Element AI to strengthen its presence in the Artificial Intelligence (AI) research landscape and the Canadian AI ecosystem. This acquisition enabled the development of AI-driven products that improve the platform’s capabilities. ServiceNow Research has made significant contributions to the field of foundation models, notably in Natural Language Processing (NLP), and has a strong presence in developing generative models for different data domains.
(2) the challenges the partner aims to solve through this project
The project seeks to address the security and safety challenges posed by autonomous AI agents in enterprise settings. Specifically, it aims to mitigate risks such as data exfiltration, prompt injections, privilege escalation by developing system-level defenses that make AI agents secure against adversarial attacks. The goal is to maintain the utility and autonomy of AI agents while safeguarding against security threats from AI agents operating in untrusted environments.
(3) the anticipated social or economic benefits of the project for the partner organization(s)
The project is expected to enhance the security of autonomous AI agents, which will safeguard sensitive data and protect against malicious attacks. This will not only boost productivity and efficiency in enterprise settings but also foster a safer digital environment. Ultimately, the opensource release of the findings of this project will contribute to the broader community by setting new standards for AI security and promoting trust in AI technologies.

Algorithms and Software System for Analysis of Twitter Data using Apache Spark

The goal of this project is to develop a software system to collect, store, organize and query Twitter messages, and to develop algorithms that can process the Twitter data to extract value-added information, in particular, the geolocation of Tweets. First, we will design and implement a processing and analytics system for Twitter data using the Apache Spark environment. Second, we will research and extend advanced algorithms to infer the geolocation of Tweets from their contents. This will benefit Spotzi as they are developing and selling new media analysis products with a focus on geography-related applications. Novel use of the Spark environment will allow Spotzi to expand its services to a larger scale in terms of data sizes and processing capacity, by relying on the scalability of Spark as an efficient distributed computing environment. Additionally, since many of Spotzi’s products are related to geographic information, enhancing the geolocation information of Spotzi’s data is important for the accuracy of their analytics products.

Subsurface biogeochemistry of hydrogen (H2) transformations: microbial implications for short-term energy storage

Hydrogen (H2) offers a strategic opportunity for Canada’s energy transition and more broadly is viewed as an essential part of the global solution to reducing carbon emissions and addressing climate change. Industry, governments and research agencies at regional and national scales are looking at the feasibility of H2 hubs that incorporate local logistics for H2 generation, storage and end-use. In this context, storage of H2 at scale will be a critical part in establishing H2 economies and markets. The scale of storage that is needed is realistically only available in subsurface settings, with salt caverns being considered a leading option. Since H2 is an excellent food (energy) source for microorganisms living in subsurface habitats, it is important to assess the potential interactions between “deep biosphere” microbiomes and stored H2 to understand the potential for H2 losses due to biological activity. Genomics information will lead to better predictions about the fate of stored H2 and whether strategies for mitigating microbial activity are needed.

Enhancing the Accuracy and Interpretability of Canadian Macro-Financial Tail Risk Forecasts via Multi-Quantile Deep Learning with Feature Engineering to Monitor Systemic Risks at the Bank of Canada

Crises risks are notoriously hard to quantify. Yet, when systemic crises materialize, for instance the Global Financial Crisis (GFC), the cost for the economy and the society can be huge, with protracted recessions and financial hardships for firms and households. Thus, it is essential for public authorities to monitor and proactively address systemic risks, thereby ensuring a stable and efficient financial system that can sustain economic growth and raise standards of living.

In this context, the Bank of Canada seeks to leverage advanced tools such as artificial intelligence and machine learning to keep improving its assessment of systemic risks. The purpose of this joint project with the academic partners is to develop state-of-the-art forecasts of macro-financial tail risks, capturing extreme shocks like those seen during the GFC.

The development of self-powered wearable biosensors

The proposed project aims to develop a wearable biosensor for real-time monitoring of critical cardiovascular disease (CVD) biomarkers, such as C-reactive protein, Troponin I, and Myoglobin, along with physiological parameters like heart rate. The biosensor will achieve high selectivity and durability by utilizing molecularly imprinted polymer (MIP) technology. The intern will collaborate with Professor Joseph Wang to integrate these biosensors into wearable devices, enabling continuous monitoring and early diagnosis of CVD. This project will benefit participating institutions by advancing wearable health technology, improving early CVD detection, and contributing to digital healthcare innovations.

ESROP – Osaka – Systems Optimization and Decision Making

This project will investigate new methods for improving decision-making tools that help organizations make better choices when faced with uncertainty. Specifically, we will study how to more accurately estimate weights in the Analytic Hierarchy Process (AHP), a common tool used for Multi-Criteria Decision Making (MCDM). By focusing on interval and fuzzy weight estimation, the project aims to create more reliable and efficient decision-support systems. The results will benefit participating institutions by providing better tools for complex decision-making, helping them prioritize actions and allocate resources more effectively in areas such as environmental management, policy planning, and other fields that require careful evaluation of multiple factors.

Segmented Seal Project

This research seeks to improve gas turbine engine components, focusing on dynamic seals that withstand extreme conditions, such as high temperatures, pressures, and abrasive particles, to enhance aircraft performance and durability. Current carbon-based materials used in seals like segmented seals—designed to prevent oil leakage—are vulnerable to degradation, which can lead to engine damage and costly maintenance. Although progress has been made in understanding their mechanical properties, there is limited knowledge on how these materials perform tribologically in realistic engine environments. This study aims to bridge this gap by employing a Category IV test rig for comprehensive testing, more closely simulating the harsh conditions seals experience in engines.

3D Point Cloud Foundation Model Project

Professor Kim’s team is advancing AIST’s initiative to develop a Foundation Model for Computer Vision, a transformative AI system designed to address diverse tasks through large-scale pre-training. These models are vital for computer vision, which focuses on enabling machines to interpret visual data like images and 3D point clouds. Professor Kim’s work centers on 3D point clouds, which are critical for applications such as forest management, urban planning, and terrain analysis. I will contribute my extensive expertise in 3D point cloud processing—supported by multiple publications on 3D semantic segmentation and digital twin development—to enhance the team’s ability to develop deep learning models for tasks such as tree species classification, terrain scene recognition, and point cloud semantic segmentation.

This collaboration will deliver valuable benefits to both institutions. For York University, the project will expand expertise in scalable, generalizable AI systems and interdisciplinary research in forestry, geography, and urban planning. For AIST, York University’s specialized skill set will strengthen the team’s capacity to tackle complex challenges in 3D data processing and advance the development of robust foundation models. This partnership will enhance research capabilities, foster innovation, and drive impactful advancements in AI and its applications.