Innovative Projects Realized

Analyzing Explanatory Texts from the Perspective of Functional Discourse Grammar

This project focuses on how people use modifiers to explain abstract terms in English and Chinese, employing Functional Discourse Grammar (FDG) to describe the linear positions of modifiers, the levels of meaning they target, and whether they affect truth conditions. Aimed at education, public communication, and language technology, it will deliver clear hierarchy–scope–linearization criteria and aligned annotation guidelines, with potential benefits across these areas. It has especially strong potential in communication-intensive domains such as law, healthcare, and narrative services. The project will use multilingual corpora and learner data to drive theoretical development, potentially advancing Canada’s multilingual ecosystem.

Novel techniques to refactor software systems using reinforcement learning

Refactoring is an important software development activity that employs various techniques to enhance the structure and quality of source code without altering its functionality. Over the last two decades, researchers in the field have proposed several tools and techniques to enable automated refactoring. Despite a plethora of literature, though some steps can be automated, overall refactoring remains a manual process. In this project, we aim to develop a method to generate refactored code from any given code to improve its maintainability using reinforcement learning.

Machine Learning-Based PV and Load Forecasting for Power System Optimization: Using real data in Cambodia with IEEE 9-Bus Validation

The growing number of renewable energy sources in Cambodia, such as solar farms, makes it more difficult to schedule power system operations to satisfy demand and save operating costs. Forecasting is one way to manage energy. An excessively high prediction accuracy indicates that managing the spatial energy in Economic Dispatch (ED) and Optimal Power Flow (OPF) is simple. One graduate intern will travel to Concordia University to conduct advanced research on machine-learning-based photovoltaic (PV) and load forecasting models using data from Electricité Du Cambodge (EDC) in Cambodia and the weather variable chosen from public data from NASA POWER in collaboration with the IEEE 9-Bus. The expected outcomes are improved forecasting accuracy, reduced grid power losses, reduced energy costs, and increased use of renewable energy. The findings will support Cambodia’s transition to clean energy and aid in the planning of a more reliable and sustainable power system. The project will also help Canada become a leader in renewable energy innovation, improve cooperation between Cambodia and Canada, and exchange new research findings.

Use of bio-inputs in the ecological restoration of iron ore mine sites in northern Quebec, Canada

This research project focuses on restoring the land that has been disturbed by post-iron-ore extraction sites at the Timmins 7 site in Newfoundland and Labrador Province and the other at the Good Wood site in Quebec Province at Tata Steel Minerals Canada Ltd. (TSMC) in Schefferville. These post-mining sites have extreme climatic conditions of a harsh subarctic climate, like long, harsh winters, low temperatures, nutrient-poor soils, and short growing seasons, which create substantial challenges for vegetation establishment and make it difficult for plants to grow naturally. To solve this problem, the project will test the use of “bio-inputs,” such as beneficial microbes—fungi and bacteria that naturally help seedling roots absorb nutrients, tolerate stress, and survive in harsh environments. We will grow native boreal seedling species like Picea mariana, Betula glandulosa, and Alnus viridis ssp. Crispa in the greenhouse with and without these microbes and then plant them in two active mine restoration sites (Timmins 7 and Good Wood). By monitoring plant survival, growth, soil quality, and nutrient recovery, we aim to identify the best combination of soil amendments and microbial treatments that can speed up ecological restoration of the post-iron-ore-mining area.

Investigation of strength and performance evaluation of hybrid wide flange (WF) beam-to-box column connections with inner diaphragms

In modern structural engineering, the connection between beams and columns plays a critical role in determining the overall performance and safety of steel structures and using an inner diaphragm is to improve strength and load transfer from beam to column too. This connection of WF beam to box column with inner diaphragm is responsible for transferring loads such as bending moments, shear forces, and axial forces between the members. The study of hybrid wide flange beam to box column connections with inner diaphragms has attracted increasing attention because of their superior load-carrying capacity, ductility, and ease of fabrication in high-rise and seismic-resistant structures. We usually use this ideal for high-rise buildings, bridges, and industrial facilities to create strong, load-bearing frames and seismic-resistant structures as well. In this study, the analytical assessment of mechanical behavior such as failure modes of the connection, evaluate the strength, load-displacement and the parametric effect of diaphragm properties by using numerical simulation software called ABAQUS/CAD. The finite element analysis results showed that variations in diaphragm parameters had only a minor effect on the overall load capacity and strength. It has showed us that parametric study is very important to improve strength and failure mode of connection.

AI Agent–Driven Digital Twin Framework for Intelligent Simulation and Optimization of Demand Responsive Transit (DRT) Systems

This project aims to develop an optimal Agentic AI Framework for next-generation intelligent mobility systems. Rather than focusing on a single algorithm or application, the research seeks to establish a generalizable architecture that integrates advanced optimization methods with graph-based large language model orchestration. Demand-Responsive Transit (DRT) serves as the primary testbed due to its inherent operational complexity, but the framework is designed to support a broad range of urban mobility services.

The core contribution lies in unifying three methodological components:
(1) an agentic control layer that interprets natural-language inputs and coordinates multi-step reasoning using a LangGraph-based structure;
(2) an optimization layer that adapts routing, dispatching, and policy parameters through formal algorithmic models; and
(3) a digital-twin simulation layer that evaluates system behaviors within an interactive urban mobility environment.

By integrating linguistic reasoning with algorithmic optimization, the project addresses a key gap between human decision intent and computational execution in current mobility systems. The resulting framework offers a scalable and extensible foundation for analyzing complex transportation scenarios, supporting evidence-based planning, and advancing research on AI-driven urban mobility.

TruEffect Digital Data Workflow, Exchange, and Analysis Innovation Project

This project aims to modernize Trueffect’s end-to-end data workflow by designing and enhancing a secure digital platform for client data exchange, validation, and analysis. The organization currently handles complex analytical datasets from multiple client partners, requiring a more efficient and scalable approach to ingestion, quality assurance, and internal analyst workflows. The proposed initiative introduces a structured and innovative system that enhances operational efficiency, strengthens data governance, and supports future analytical capabilities.

The intern will work with the academic supervisor and Trueffect to advance a secure web-based platform that integrates authentication, file ingestion, exploratory data analysis (EDA), and structured data-quality reporting. The project includes mapping current processes, identifying inefficiencies, and implementing improvements that streamline client uploads, reduce manual analyst workload, and increase reliability. The intern will contribute to architecture refinement, feature implementation, and workflow optimization, including enhancements to validation routines, shared-project synchronization, and the end-user experience.

IoT device identification using continuous machine learning models

In this project, we will design and develop an IoT device auto-detection model and deploy it on a Raspberry Pi. The model will follow a continuous machine-learning approach and will be trained using an IoT dataset developed in our lab. The Raspberry Pi will function as a Wi-Fi router, allowing IoT devices to connect to it. Upon connection, the system will automatically identify each device—including its vendor and model—in real time.

Benchmarking Post Quantum Cryptography in Internet of Things

In this project, students are expected to set up a testbed and run experiments to evaluate the impact of integrating post quantum cryptography (PQC) into IoT devices. The evaluation metrics include CPU usage, memory usage, bandwidth usage, and energy usage. Various PQC mechanisms will be evaluated on MQTT over TCP+TLS, including ML-KEM, HQC, ML-DSA, and SLH-DSA. A performance benchmark is expected to be created for PQC efficiency on IoT devices with this project.

Modélisation musculosquelettique des chutes en escalade de bloc

Ce projet concerne la sécurité en escalade de bloc, un sport dans lequel les chutes sont inévitables et difficiles à prévoir. L’objectif est de mieux comprendre les réactions du corps lors de différents types de chutes, afin d’identifier les situations susceptibles d’entraîner des blessures. Le projet s’appuie sur une base de données composée de chutes réelles de jeunes grimpeurs et grimpeuses, filmées dans des conditions contrôlées. À partir de ces vidéos, des simulations numériques seront réalisés afin de reconstruire le mouvement du corps, d’estimer les muscles sollicités et les efforts subis par les articulations lors de l’impact. Ces simulations musculosquelettiques permettront d’étudier en détail l’influence de paramètres clés, comme l’orientation du corps, la vitesse d’arrivée ou la façon dont la personne se réceptionne. En identifiant les scénarios qui sollicitent le plus certaines articulations ou groupes musculaires, il sera possible de repérer les types de chutes les plus à risque. Ces résultats permettront d’améliorer la prévention des blessures en escalade, par exemple en ajustant les recommandations d’entraînement et en perfectionnant les techniques de réception. Ce projet vise donc à mieux protéger les grimpeurs et grimpeuses grâce à une compréhension précise des chutes et de leurs effets sur le corps.

Implementation of 6-degree-of-freedom visual tracking for robotic in situ bioprinting

This project focuses on developing a tracking system for in situ 3D bioprinters. These machines are designed to 3D print implants over the body, and the tracking system enables them to determine the location of the target surface, as well as adjust their motion to accommodate movements from this surface. Both York University and the University of Pisa are working on this subject using the same printing system, and they will collaborate on creating a robust and versatile tracking system to advance their research on in situ bioprinting.

Implementation of multi-centre clinical trials in orthopaedic trauma

This research internship is offered within the Division of Orthopaedic Surgery under the supervision of Dr. Sheila Sprague, PhD. The program focuses on clinical research aimed at improving outcomes and quality of life for patients with musculoskeletal injuries through multiple ongoing clinical trials, including Beads vs. Vac, FLAP-ATTACK, FASTER-HIP, SWRCT, and studies on pelvic fracture management (PIVOT-LC1 Pilot, FLIPER, and ATV).

The internship provides an opportunity to gain experience in health research methodology and clinical trial implementation within the orthopaedic trauma research team. The intern will work closely with project managers and research coordinators to learn daily study operations and contribute to multiple aspects of clinical research management.

Key responsibilities include drafting and editing study documents (procedures, reports, manuals), reviewing and validating study data, managing data queries, maintaining trial master and investigator site files, updating study trackers, and preparing documents for adjudication committees. Additional duties include organizing study meetings, recording minutes, tracking action items, communicating with clinical sites and stakeholders, and supporting manuscript preparation, statistical analyses, and grant proposal development.

This internship offers comprehensive exposure to clinical research coordination and the practical management of multicenter trials.