Celebrating Our Graduates

The School of Graduate and Postdoctoral Studies (SGPS) is proud of the accomplishments of our graduates and excited to showcase the diverse and impactful research they have undertaken and acknowledge their contributions to Ontario Tech's rich culture of innovation and excellence. To celebrate their achievements, we are pleased to present a selection of our recent graduates and details regarding their completed studies.

Ali Jeiranzadeh
Master of Mechanical Engineering
Ali's thesis presents a transient CFD study of a Stirling engine with angled passageways embedded in the displacer. A moving-boundary model with a prescribed dwell period was developed to better represent real engine behaviour. Parametric studies examined the effects of passageway angle, opening size, radial position, and displacer speed on heat flux at the hot plate during dwell. Results showed that embedded angled passageways significantly improved heat transfer compared with a design without passageways. Angle of passageways, opening distribution, total area, radial placement, and operating speed affected heat transfer, providing guidance for efficient passageway design in compact Stirling engines.

Arda Yapicioglu
Doctor of Philosophy (PhD) in Mechanical Engineering
Arda's research focused on the development and optimization of renewable energy-based multigenerational systems that simultaneously produce electricity, heating, cooling, freshwater, and hydrogen, with integrated energy storage solutions. The study combines advanced thermodynamic modelling with quantum-enhanced optimization techniques to improve efficiency, reduce emissions, and support global net-zero targets. Through case studies in California (USA), Nunavut (Canada), and Izmir (Türkiye), the research demonstrates how integrated renewable systems can be adapted to diverse climates and energy demands, contributing to resilient and sustainable energy solutions for future communities.

Astrid DeSouza
Doctor of Philosophy (PhD) in Health Sciences
Title: Exploring the Prevalence and Factors Associated with Pain-Related Disabilities Among Indigenous Peoples in Canada: A Scoping Review and Cross-Sectional Study of the 2017 Aboriginal Peoples Survey.
Pain-related disabilities (PRDs) are among the most commonly reported disabilities in Canada, yet little is known about their impact among Indigenous populations. This work was guided by the principles of Etuaptmumk (Two-Eyed Seeing) and conducted in partnership with an Indigenous Advisory Committee.
This project was funded by CIHR'S CGS Doctoral Award through the Institute of Indigenous Peoples’ Health, and through the Canadian Chiropractic Research Foundation (CCRF).

Bryan McLaughlin 
Doctor of Philosophy (PhD) in Health Sciences
There has been considerable amounts of research in sport emphasizing the interaction between the individual and their environment, but relatively little attention has been paid to the compatibility between the two. Drawing upon person–environment fit theory and implementation science principles, Bryan's dissertation explored a novel approach to athlete development by investigating athlete-environment fit conceptually, empirically, and practically. This work led to the development and implementation of the Athlete Fit Tool (AFT), an evidence-informed, user-friendly assessment that enables coaches, athletes, and practitioners to gauge how well an athlete fits with their current sport environment.

Cagri Turk
PhD in Mechanical Engineering
My PhD research focused on advanced electrochemical energy systems. I investigated ammonium formate as a sustainable hydrogen carrier and fuel, developed novel electrochemical cell designs, and studied advanced electrode and electrolyte materials for energy storage applications. My work combined experimental electrochemistry, materials science, and energy engineering to improve the performance of future clean energy technologies.

Dylan Rapanan
Master of Science in Computer Science
Dylan's thesis investigated how the brain processes emotions in real versus virtual faces through functional near-infrared spectroscopy (fNIRS). Findings revealed that while participants perceived emotional meanings consistently across both face types, neural responses differed significantly. Virtual faces primarily triggered localized activity in the left occipital regions, whereas real faces elicited robust, widespread functional connectivity across frontal, temporal, and parietal networks. High-arousal emotions like fear and joy also engaged broader networks. Ultimately, while basic activation is similar, the brain processes real and virtual faces through distinct network dynamics, suggesting digital realism depends on more than just visual accuracy.

Emon Roy
Master of Science in Computer Science
Emon's research examines how reliable Grad-CAM explanations remain when input images are transformed. Since CNN-based image classification is widely used, understanding explanation stability is important. The study evaluates Grad-CAM under rotation, zoom, and horizontal and vertical shifts across ResNet152, DenseNet201, and Xception. A controlled experimental pipeline was developed to generate transformed images, produce heatmaps, and measure consistency using Euclidean (L2) difference and AUC-ROC. Results show that Grad-CAM heatmaps often change under transformations, reducing reliability. To address this, the research proposes heatmap averaging methods that create more robust and transformation-invariant explanations, improving the trustworthiness of CNN interpretation in practical applications.

Ingi Hong
Master of Arts in Education
Ingi's research explored the integration of digital media into early learning environments in Ontario, focusing on educators’ practices, supports, barriers and professional learning needs.

Maher Muhtadi
Master of Applied Science in Software Engineering
My research investigated adaptive self-prompting in agentic large language model (LLM) frameworks for fault detection in embedded C code. I developed and evaluated two complementary frameworks that enable LLMs to adapt their reasoning by estimating uncertainty, selectively retrieving guidance from software security and coding standards, and refining their analysis when needed. Using a unified benchmark of synthetic and real-world vulnerable code, my work demonstrated improved fault detection performance and reduced overconfident misclassifications compared with static prompting approaches. This research advances reliable AI-assisted static analysis for safety- and security-critical software systems.

Mosarrat Rumman
Master of Science in Computer Science
Rather than asking whether foundation models (SAM) or specialized AI models (nnU-Net) are better for medical imaging segmentation, Mosarrat's master's research explored how to combine the best of both. She developed an uncertainty-aware fusion framework that allows different AI models to work together. Rather than simply combining predictions from multiple models, the framework evaluates how confident each model is and decides whether to fuse their predictions or rely on the more trustworthy one. This approach improved cardiac MRI segmentation by up to 22% in Dice score on unseen data, producing more accurate and reliable results than either model alone.

Muhammad Zaeem Khalid
Master of Applied Science in Software Engineering
Muhammad's thesis investigates energy-efficient platooning of Connected and Autonomous Vehicles(CAVs) in simulation frameworks using energy modelling. A lightweight physics-based energy model was integrated into a framework to capture kinematics data, such as aerodynamic drag and inertial dynamics in real time. This thesis focuses on evaluating multiple control strategies for energy efficiency under controlled scenarios. Three controllers were evaluated: Adaptive Cruise Control(ACC), Cooperative Adaptive Cruise Control(CACC), and proposed energy-aware controller. The energy-aware controller provided greater energy savings than the other two controllers with savings over 20% along with strongest string stability.

Samuel Canonaco
Master of Science in Computer Science
Samuel's thesis focused on how game developers use haptic technology in their games. Samuel created a custom piece of software that allowed haptic effects to be programmed onto a DualSense controller and ran a study with student developers to prototype haptic effects that represented a variety of different common game related scenarios and compared how they were able to iterate and prototype their effects in different software interfaces. This provided a lot of information on the challenges of working with haptics, and potential ways to improve the process of adding haptics into games in the future.

Sara Taghavi Motlagh
Doctor of Philosophy (PhD) in Computer Science
Sara's thesis enhances how communication networks respond to disasters, where failures evolve over time and disrupt essential services. It develops a dynamic, service-aware optimization framework that allows networks to adapt in real time, prioritize critical services, and manage risk under strict capacity constraints. By integrating reliability, economic impact, and service-level requirements into a single model, it provides a practical decision-support tool for service providers. The results show how adaptive routing can reduce disruptions, protect high-priority traffic, and improve overall network performance. This contribution offers a stronger foundation for building resilient, reliable communication systems in an increasingly uncertain world.

Shannon Shackles
Master of Arts in Education
Public speaking is a vital skill, yet it frequently triggers significant anxiety in adolescents. Shannon's mixed-methods case study explored the use of Virtual Reality Exposure Therapy (VRET) as a tool to support high-school students (ages 14–18) dealing with public speaking anxiety (PSA). By integrating VR practice into classroom instruction, Shannon analyzed quantitative self-reports and qualitative reflections to evaluate student outcomes. Shannon's findings suggested that VRET reduced PSA symptoms and strengthened skill-building. Ultimately, demonstrating that VRET served as a powerful complementary tool for reducing anxiety and fostering confidence in the classroom.

Somayeh Foroughi
Doctor of Philosophy (PhD) in Electrical Engineering
Somayeh's research focuses on improving the performance and efficiency of next-generation wireless communication systems using reconfigurable intelligent surfaces (RIS). Somayeh studied how multiple RISs can be jointly configured with system parameters, such as transmit power, to enhance signal quality and data rates. Somayeh's thesis developed optimization methods that coordinate these components in a unified framework. Unlike many existing studies that rely on simplified assumptions, Somayeh's work incorporates more realistic models where RIS elements can adjust both signal amplitude and phase. Overall, contributing practical design insights and efficient techniques to improve performance of future wireless networks.

Tomas de Melo
PhD in Applied Bioscience
Understanding the dynamics and drivers of viral pathogens within human populations is essential for informing timely public health interventions, especially during periods of concern. This study focused on enhancing community-level viral surveillance by monitoring Influenza A, Norovirus Genogroup II, and Enteroviruses and further sequenced Norovirus GII and Enterovirus RNA to ascertain how their diversity drives their prevalence. Overall, this research demonstrates the value of integrating molecular, statistical, and sequencing methods for comprehensive and actionable public health surveillance, ultimately providing high-quality data to support effective public health decision-making and to build a participatory approach to public health.