Innovative Projects Realized

Physics-informed machine learning for cryogenics optimization

During this Accelerate internship, we will push ZPC’s current dilution refrigerators to new heights of performance and reliability by harnessing modern machine-learning and AI techniques.

L2M Validation / Qc Automne 2025 / AsbabAI is a novel platform that evaluates the causality of deep learning models to improve their explainability and robustness using causal inference,a key requirement in regulated sectors such as healthcare and finance

Artificial intelligence (AI) is increasingly used to make high-stakes decisions in fields like finance, insurance, and healthcare. However, most of AI systems function as “black boxes,” producing results without clear explanations. This lack of transparency can lead to serious consequences, such as denying loans to creditworthy individuals or making unfair risk assessments.

The AsbabAI aims to make AI models more transparent, reliable, and robust. Using advanced techniques called causal inference, it helps uncover the real reasons behind a model’s decisions and ensures those decisions remain valid even when real-world conditions change.

This project will be especially valuable for financial institutions, which face growing regulatory pressure to ensure fairness and explainability in their AI systems. It also supports public trust in AI and contributes to Canada’s leadership in responsible AI innovation.

A wireless headset for power-aware EEG/non-EEG signals processing and seizure warning

Epilepsy is a debilitating neurological disorder affecting approximately 50 million people worldwide (World Health Organization). This project proposes to develop a SMART (Seizure Monitoring At the Right Time) Headset for these patients to monitor their brain signal and track their movements, heart rates and breathing patterns. This headset will have new types of sensors suitable for long term usage and sleep. An intelligent micro-chip in the headset will track patient continuously and detect epilepsy syndromes, and warn patient and caregiver in advance (e.g., few seconds to minutes), which may prevent a patient’s life threatening accident. This SMART Headset will connect globally through patient’s mobile device and allow the clinicians monitor the patient remotely 24-hr/7-day with less clinical expense and government healthcare cost. This device will provide continuous personal assistance and may allow patients to work full-time and live independently, and reduce government burden in healthcare and boost economic growth.

L2M – EnerCast

I intend to commercialize a foundation model for forecasting energy production and consumption time series, addressing challenges like renewable integration, storage optimization, and demand-supply balancing. Resources such as HVACs, EVs, ESSs, and solar roofs introduce uncertainty due to temperature sensitivity, price response, and weather variability, making grid management and the transition to smart grids more complex. Our model incorporates external factors like outages and extreme events to improve forecast accuracy and mitigate peak prices. We also aim to build automated energy management systems that reduce customer costs while helping system operators balance the grid. Additionally, we plan to develop an LLM-based chatbot that offers intuitive, AI-driven insights for market participants and energy traders, making advanced forecasting accessible without requiring technical expertise.

L2M – IoSL: A Geospatial-aware Internet of Safe Labourers for Assessing Workers’ Well-being and Safety Improvement

This project will develop a plan to bring a new worker fatigue monitoring system to market. The system uses wearable sensors and artificial intelligence to detect when workers are becoming tired, so that action can be taken to prevent accidents and keep people safe on the job. During this project, market research will be done to understand customer needs, and strategies will be created for testing the system in real workplaces and launching it as a business. The expected benefit to the partner organization is a clear and practical roadmap for successfully commercializing this technology, thereby improving worker safety and productivity in industries such as construction, mining, and transportation.

L2M – Vira

This research-based project aims to make developing virtual reality (VR) applications faster and easier by using generative AI tools. By streamlining the development process, the project can help both companies and individual developers save time and resources. This is especially valuable for Calgary’s growing community of VR businesses and research labs. Overall, the project supports Calgary’s and Canada’s innovation ecosystems by introducing new ways to boost productivity and creativity in VR development.

L2M – Clinical Trial Recommendation System Using Large Language Model

We are developing a conversational AI system that helps hospitals and cancer centers find the right patients for clinical trials much faster and easier. Currently, research staff spend weeks manually reading through hundreds of pages of patient medical records to see if patients qualify for specific cancer treatment studies, which means many patients who could benefit from new treatments are never found or found too late. Our AI agent can automatically read and understand patient records, medical notes, and lab results, then have natural conversations with healthcare workers to recommend which trials might be good matches for each patient. This system will save significant time and money by reducing the manual work needed to screen patients, help more cancer patients access potentially life-saving treatments, and enable smaller hospitals to participate in clinical research that they couldn’t handle before due to limited staff resources. The conversational interface makes the technology easy for busy healthcare professionals to use without extensive training, ultimately improving patient care while reducing administrative burden on medical staff.

L2M – Digital Twin for Real-Time Cardiac Arrest Risk Monitoring

This project has developed a real-time digital monitoring system that identifies people at risk of sudden cardiac arrest by analyzing their heart signals (ECGs). By combining a machine learning model with cloud-based technology, the system automatically updates each patient’s status and sends alerts if a risk is detected. This enables the partner organization to provide faster, more accurate patient monitoring in hospitals or remotely, allowing earlier interventions that could save lives. The system makes heart monitoring more proactive and personalized, improving patient care and safety.

City of Abbotsford Body Worn Cameras Project

As Abbotsford Police Department begins the implementation of the trial phase of their police body-worn camera (BWC) program, a thorough evaluation of the program is needed. The Community Health and Social Innovation (CHASI) Hub will support the Abbotsford Police Department’s work by engaging in the research activities to conduct this evaluation.

L2M – TraffiCore

During the course of this project, the main focus will be on the market research aspect of TraffiCore, a traffic monitoring and control automation startup which aims to provide traffic managers and controllers with a useful tool that can automatically perform the repetitive and mundane tasks that are usually perfomed manually and suboptimally. The intern will conduct interviews with potential customers and users in both the public and the private sectors, validate the problem the startup tackles, validate the developed solution’s desirability, build a business model, and identify risks and competitors. At the end of this program, the solution should have been validated, the understanding of the market enhanced, and the business plan clearly defined.

L2M – Development of a Rapid Molecularly Imprinted Polymer (MIP)-Based Diagnostic Device for Differentiating Bacterial and Viral Infections via Dual Biomarker Detection

We are developing a rapid, low-cost diagnostic device that helps healthcare providers quickly determine whether an infection is bacterial or viral, leading to better treatment decisions and reduced antibiotic misuse. This project will explore real-world demand for the device, evaluate its competitive edge, and refine its market strategy. Through the Lab2Market Validate program, Edmonton Unlimited will support the translation of university-based research into practical healthcare solutions, contributing to the development of a stronger and more effective health innovation landscape in Alberta and beyond.

Using Ergonomic AI software to enhance PDA and Ergonomic Programs

Optimization of an ergonomics program within a manufacturing facility. AI camera based system for postural injury risk identification will be utilized to improve productivity and quality of injury risk identification. The project will then continue to applying the RACE Recognize, Assess, Control, Evaluate model. Physical Demands Analysis and process and design solutions for risk reductions will be proposed. Occupational injuries are a significant burden on Canadian manufacturing companies. Improved identification and ergonomic solutions will improve working conditions for Canadians. .