Celebrating Our Graduates
Fall 2024
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.
Jakob Anderson
MSc (Computer Science)
Volumetric video is a relatively new medium that enables creators to capture a 3D video of the real world. Despite over a decade of development, there remains several hurdles that discourage its use, such as file sizes. Many works have constructed pipelines for compression of volumetric video, however, there are noticeable gaps in areas pertaining to temporal compression. This work seeks to address part of this gap, and experiments with temporal compression of volumetric video using signed distance field sequences. The results show that there are improvements over the state-of-the-art with respect to quality as the compression ratio increases.
Victoria Ginsley
PhD (Criminology and Social Justice)
Victoria's dissertation examines transgender people’s experiences and perceptions of police within Canada. Her study investigates the need for a transgender lens when examining the concepts of procedural justice and police legitimacy. Findings from her research contribute to the growing literature on gender and legitimacy by emphasizing the need for transgender-focused analyses. Additionally, findings show that trans people have limited or no trust in the police, as individual officers or in the institution of policing. Trans people feel the only way to address police mistreatment is to abolish the policing system.
Md Asif Khan
PhD (Electrical & Computer Engineering)
My thesis focuses on Test case prioritization using machine learning. In current CI/CD pipeline testing is a crucial part and the number of test results are enormous. By prioritizing test case based on their probability of failure produced by Ml model we try to find failure earlier and improve software development process.
Irmina Klicnik
PhD (Health Science)
My dissertation focused on active aging, defined as the interplay between social engagement and movement behavior, across three distinct environments: community, assisted living, and hospital settings. Using a mixed-methods approach and the social-ecological model, I examined how environmental factors influence social participation and physical activity among older adults. The studies revealed key insights into how walkability, greenness, and interventions aimed at reducing sedentary behavior impact both social engagement and physical activity. Findings highlight the importance of considering diverse living environments to enhance active aging, and guide future interventions for older adults.
Manraj Singh Ladhar
MASc (Electrical and Computer Engineering)
My thesis examines optimal design principles and practices (Signal and Power Integrity) essential for the Printed Circuit Board (PCB) layout of switching regulators with fast dV/dt and dI/dt edge rates to achieve Electromagnetic Interference (EMI) Compliance standards. Reflections, crosstalk, and transmission line management techniques along with Power Delivery Network/System (PDN/PDS) design are implemented for enhanced Signal and Power integrity.
Amit Maraj
PhD (Computer Science)
This thesis explores novel supervised deep learning models for text segmentation (TS), outperforming previous unsupervised methods. Using BERT encodings and a labeled corpus, the system achieves improved Pk and WindowDiff scores. Integrating LDA-derived features with sentence embeddings yields comparable results to state-of-the-art systems and enhances generalization. GPT-based text augmentation further boosts performance. Additionally, an unsupervised TS system, "Coherence," leveraging strong embeddings and unsupervised features, achieves competitive results with fast inference and no fine-tuning, addressing the gap in unsupervised TS research.
Roxana Pao
PhD (Mechanical Engineering)
It is dangerous to commute in adverse weather due to impaired visibility. With the rising trend of autonomy, it is important to understand vehicle sensor performance to deploy strategies for mitigating the risks of degraded perceptions. My research involves developing realistic, controlled, and repeatable weather testing methodologies, such as implementing a novel precipitation simulation concept in wind tunnels. My thesis focused on automotive LiDAR performance when driving in rain using different sensor lens materials. In this work, I studied the interactions between the raindrops, aerodynamics, surface material properties, and optical rays.
Amy Petch
MSc (Applied Bioscience)
My Master of Science thesis project focused on developing in-vitro methods for investigating novel drug targets within microfilaria parasitic nematodes. This quantitative phenotypic analysis was induced by small molecules and dsRNA gene silencing, targeting receptor pathways within the nematode nervous system.
Eyad Tamimi
MASc (Nuclear Engineering)
My research focuses on shielding optimizations and dosimetry for future manned space missions. For humans to venture out beyond the protective cusp of earth's magnetic field, current shielding technologies are one of the major limitations affecting mission timelines. I am continuing this research at the PhD level where I aim to perform accelerator-based experiments to validate the heavy ion transport modeling performed as part of my Master's studies.
Babangida Zachariah
MASc (Electrical & Computer Engineering)
My thesis, Fuzzy Logic-Based Intersection Management for Delay Minimization in Intelligent Transportation Systems Using V2X Communication explores the application of computational intelligence tools to the problem of road traffic delays. Exploring the V2X communication technologies for real-time traffic data collection, the developed fuzzy logic-based traffic lights controller achieves minimal average delay for vehicles while improving intersection throughput compared to the traditional and a recently proposed approach.
