Please join the Computer Science department for the upcoming thesis defense:

Presenter: Jay Vinit Lunia

Thesis title: Trustworthy Learning with Graph-Aware Quantum-Classical Transformers for Hyperspectral Imaging and NLP Tasks

Abstract: This thesis develops a unified framework for trustworthy and efficient learning under constrained representations, with experiments spanning hyperspectral image understanding and natural language classification. The central premise is that representation constraints should be designed as explicit modeling choices rather than treated as unavoidable limitations. Following this premise, the thesis builds architectures that combine structured inductive bias, compact latent interfaces, and reliability-centered evaluation. For hyperspectral learning, the proposed methodology integrates patch-level graph construction with transformer attention to preserve local spatial coherence while still modeling long-range spectral interactions. This design is extended to multitask prediction by sharing a constrained backbone across classification and regression objectives. For compact hybrid modeling, the thesis introduces a low-dimensional quantum–classical token interface in which a variational quantum encoder acts as a structured bottleneck before lightweight transformer blocks. For reliability analysis, the thesis evaluates not only aggregate predictive accuracy, but also adversarial sensitivity, explanation stability, and cross-setting consistency under matched interface constraints. The empirical study covers multiple benchmark settings with heterogeneous spectral characteristics and class imbalance profiles, together with text classification tasks under fixed encoder conditions. Results show that the proposed constrained architectures achieve strong predictive performance while maintaining stable behavior across datasets and tasks. In hyperspectral experiments, the graph-aware transformer pipeline produces consistently high class-level and global metrics. In compact hybrid experiments, the quantum-classical interface remains competitive at low parameter budgets and reveals distinct robustness and attribution patterns when compared with a matched classical head. Overall, the thesis establishes a single integrated claim: principled representation constraints can improve reliability, interpretability, and deployment practicality without requiring a trade-off against competitive predictive quality. The work contributes an end-to-end blueprint for designing constrained yet trustworthy learning systems, including architecture design rules, evaluation protocols, and practical guidance for future extensions to stricter generalization tests and hardware-grounded hybrid inference.

Committee members:
Dr. Saad B. Ahmed (supervisor, committee chair), Dr. Garima Bajwa, Dr. Thangarajah Akilan (Software Engineering)

Please join the Computer Science department for the upcoming thesis defense:

Presenter: Mohammed Salman Khan

Thesis title: Crop Disease Analysis through Hyperspectral Images Using Deep Learning Models

Abstract: Modern precision agriculture increasingly relies on high-resolution Unmanned Aerial Vehicle (UAV) hyperspectral imagery to map diverse vegetation species and monitor complex crop health. However, processing these massively high-dimensional data cubes historically requires classical deep learning models with unsustainable computational bloat, such as heavy vision transformers or extremely deep convolutional networks. Furthermore, standard optimization pipelines routinely collapse when confronted with the complicated structural complexities of real-world agricultural datasets, which naturally feature severe class imbalances and highly overlapping spatial boundaries. This thesis directly attacks these critical computational and mathematical vulnerabilities by engineering ultra-lightweight, parameter-efficient hybrid quantum-classical architectures. By entirely replacing massive classical dense layers with a parameterized 4-qubit variational quantum circuit, this research demonstrates that quantum mechanics can natively and efficiently synthesize the highly complex, non-linear global dependencies required for accurate field classification.

To overcome the distinct spatial and spectral challenges of agricultural data, this work introduces two novel evolutionary frameworks. The first, the Quantum Patch-Graph Transformer (QPGF), mathematically preserves orthogonal crop row geometry by structuring spatial patches into row-normalized 4-nearest neighbor graphs, seamlessly fusing local graph attention with quantum global feature extraction. The second methodology is the Quantum Enhanced CNN-BiSpectralMamba-Quantum architecture, which actively bypasses standard memory bottlenecks by utilizing bidirectional Mamba state-space models to aggressively process continuous spectral sequences at linear complexity. Both architectures are stabilized by a custom Hybrid Cross-Entropy and Log-Cosh Dice loss function. This highly specialized optimization pipeline strictly forces the networks to penalize dominant staple crops and accurately map the topological boundaries of rare, minority vegetation.

Rigorous empirical validation on the highly imbalanced, 200-band, 30-class UAV-HSI-Crop dataset proves the absolute efficacy of these hybrid designs. The classical-quantum fusion drastically reduced the total trainable parameter count compared to state-of-the-art classical benchmarks. Despite this incredibly lightweight computational footprint, the QPGF established a robust baseline of 81.92% Overall Accuracy, while the advanced Quantum Enhanced CNN-BiSpectralMamba achieved a highly competitive peak of 84.83% Overall Accuracy and 82.07 Kappa score. Ultimately, this thesis proves that fusing targeted classical spatial-sequence extractors with quantum state entanglement provides a mathematically elegant, highly scalable, and resource-efficient diagnostic engine for the future of precision agriculture.

Committee members:
Dr. Saad B. Ahmed (supervisor, committee chair), Dr. Abedalrhman Alkhateeb, Dr. Ehsan Atoofian (Electrical & Computer Engineering)

The Social Justice Studies Research Day Presentations will take place on April 22, 2026, at AT5041 (Thunder Bay Campus) and OA2020 (Orillia Campus), with a Zoom option available.

To receive the Zoom link, please email Admin.socialjustice@lakeheadu.ca.

This event features research and course-based presentations by undergraduate and graduate students on a range of social justice topics.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Austin McCutcheon

Thesis title: Large-Scale News Headline Quality Analysis: Clickbait Trends, Binary Classification, and AI-Generated Content

Abstract: Online news can be characterized by massive volumes of news content spanning a spectrum from high-quality professional journalism to low-quality articles. This thesis presents four empirical studies that employ methods to analyze, classify, and evaluate quality-varying news headlines at scale.

The first two studies apply Interrupted Time Series (ITS) analysis to examine associations between clickbait prevalence and major events. Analysis of 451 million headlines from worldwide news websites (2016-2023) revealed statistically significant associations for three of five events, each showed slight pre-event decreases followed by sustained post-event increases in clickbait levels. A complementary analysis of 7.4 million headlines from Canadian news websites (2017-2023) found similar patterns.

The third study benchmarks twelve machine learning and deep learning models for binary classification of perceived news quality on a balanced dataset of 57.5 million headlines labeled according to website-level expert consensus ratings. Results demonstrated that a CPU-based Bagging Classifier achieved 88.1% accuracy with stability across cross-validation folds, while a fine-tuned DistilBERT model achieved the highest accuracy at 90.3% but required substantially greater computational resources.

The fourth study evaluates fourteen accessible Small Language Models (SLMs) for their willingness to generate fake news headlines when explicitly prompted and tests whether the trained classifiers from study three generalize to synthetic content. Minimal resistance to generating false news headlines was found, with models refusing requests less than 1% of the time. Both classifiers showed substantially reduced performance on AI-generated headlines (54-63% for DistilBERT, 35-48% for Bagging), with systematic misclassification of AI-generated “high-quality” content as “low-quality,” indicating that human-trained classifiers do not generalize effectively to current AI-generated text.

This thesis contributes the application of ITS methodology to clickbait analysis at web scale, comprehensive benchmarking of model architectures for large-scale headline quality classification, and empirical evidence that quality classifiers trained on human-authored content exhibit reduced performance when applied to SLM-generated headlines.

Committee Members:
Dr. Chris Brogly (supervisor, committee chair), Dr. Xing Tan, Dr. Xingwei (Nancy) Yang (Toronto Metropolitan University)

Please join the Department of Computer Science for the upcoming thesis defense:

Presenter: Huixiang Zhang

Thesis title: A Hybrid Quantum-Classical Architecture for Combinatorial Decision Optimization in Networked Systems

Abstract: Combinatorial decision optimization problems arise widely in modern networked systems, where limited communication, computing, and service resources must be efficiently allocated under complex operational constraints. Representative examples include supply-demand matching in data markets, topology control in self-organizing Unmanned Aerial Vehicle (UAV) swarms, and microservice scheduling across the cloud-edge continuum. These problems are typically NP-hard, and as system scale increases or operating conditions evolve rapidly, traditional Mixed-Integer Linear Programming (MILP) formulations often become difficult to solve within real-time decision windows. As a unified binary optimization framework, Quadratic Unconstrained Binary Optimization (QUBO) provides a common way to map diverse combinatorial problems to quantum annealing and quantum-inspired solvers with the potential for significant computational speed advantages. However, the practical use of QUBO in real networked systems still faces three major barriers. First, QUBO modeling remains manual, expert-dependent, and error-prone. Second, standard QUBO formulations are inherently static and therefore not well suited to time-varying environments. Third, the binary representation of QUBO does not naturally align with the continuous resource allocation requirements of real systems. To address these limitations, this thesis develops a hybrid quantum-classical optimization methodology for networked systems. It first formulates and validates domain-specific QUBO models for representative applications. Then it generalizes these efforts through two-stage hybrid frameworks that combine offline combinatorial optimization with lightweight online decision-making for dynamic UAV topology control and robust microservice scheduling. Finally, it investigates large language model driven automation of the MILP-to-QUBO pipeline and integrates Benders decomposition to improve scalability for larger problem instances. Overall, this thesis shows that QUBO can serve not only as a problem-specific solution form, but also as a transferable modeling layer that connects heterogeneous network optimization tasks with near-term quantum hardware, thereby providing a practical pathway toward quantum-enhanced decision-making.


Committee Members:
Dr. Mahzabeen Emu (supervisor, committee chair), Dr. Thiago E Alves de Oliveira (co-supervisor), Dr. Xing Tan, Dr. Elif Ak (Memorial University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.

Save the date, and plan to attend this event to support Kinesiology and Psychology students presenting their research! This is now taking place in the CASES Atrium.

Please join us for Lydia's PhD dissertation proposal defense. This is a hybrid defense, should you wish to attend in person, it will take place in ATAC 3004.

Title: Exploring First Nations-led, culturally-based resources to support wellbeing across regional and national contexts

Supervisor: Dr. Chris Mushquash

Second Reader: Dr. Alex Drawson

GSC Chair: Dr. Amanda Maranzan

Please contact admin.psych@lakeheadu.ca for Zoom link and passcode.

Please join us for George Drazenovich's PhD dissertation defense. This is a hybrid defense, should you wish to attend in person it will take place in ATAC 3004.

Title: The Linguistic Signature of Human Rights: Constructing and Testing a Cognitive Framing Model with Historical and Psycholinguistic Methods

Supervisor: Dr. Mirella Stroink

Second Reader: Dr. Josephine Tan

GSC chair: Dr. Beth Visser

Come decorate your own Easter eggs, load them up with candy, and learn about how Easter is celebrated around the world.

Lakehead University's Student Juried Exhibition and Honours Graduating Show will open to the public on the afternoon of Friday, March 27 at the Thunder Bay Art Gallery. 

The Department of Visual Arts invites you to join us for the opening and award ceremonies on March 27 at 7 p.m. Doors open at 6:30 pm.

The Juried Student Exhibition will run until April 5. The honours exhibition will run until April 12. 

More information can be found here: https://theag.ca/tc-events/2026-lakehead-university-juried-student-exhibition/.