If you’re an international student who missed our Welcome Day event on September 2, or you’d like to review this information again, please join us for one of these sessions!
If you’re an international student who missed our Welcome Day event on September 2, or you’d like to review this information again, please join us for one of these sessions!
Stop by to meet staff, ask questions, and connect with us about campus services and upcoming events!
Stop by to meet staff, ask questions, and connect with us about campus services and upcoming events!
Stop by to meet staff, ask questions, and connect with us about campus services and upcoming events!
Stop by to meet Settlement Counsellors who support newcomers across Simcoe County, ask questions about community and social services, and connect with us about upcoming webinars! If you are new to Barrie and have dependents, this team will be a great resource for you.
Stop by to meet Settlement Counsellors who support newcomers across Simcoe County, ask questions about community and social services, and connect with us about upcoming webinars! If you are new to Barrie and have dependents, this team will be a great resource for you.
While studying abroad in WALES, Baden enjoyed dancing to traditional music and exploring the many castles throughout the country. Learn about Baden’s travels and experience a piece for yourself by stopping in!
Please join the Department of Computer Science for the upcoming thesis defense:
Presenter: Tanner Boyle
Thesis title: A Leakage-Aware and Reliability-Focused Evaluation Framework for Multiclass Alzheimer’s Disease Staging Using MRI Slices
Abstract: Two-dimensional magnetic resonance imaging (MRI) studies of Alzheimer’s disease (AD) can overestimate performance when slices from the same subject appear in both training and assessment sets. This thesis evaluated a leakage-aware framework for multiclass AD staging based on subject-independent partitioning, subject-level prediction, calibration, and external validation.
Three model families were compared on 347 OASIS subjects using subject-wise crossvalidation: a convolutional neural network (CNN), a Vision Transformer (ViT), and a Hybrid CNN–ViT architecture. A matched slice-random ablation quantified leakageassociated inflation. Supporting analyses examined imbalance-aware objectives and subject-level reliability. Generalizability was tested on 502 ADNI subjects after OASIS model-selection decisions were fixed.
Slice-random evaluation contaminated 98.3% of assessment subjects and inflated macro- F1 by 0.252–0.288. Under subject-wise evaluation, the Hybrid achieved the highest OASIS macro-F1 (0.711). On ADNI, the ViT achieved the highest external macro-F1 (0.718), reversing the internal ranking. These findings show that evaluation protocol and independent cohort testing can materially change conclusions about model quality.
Committee Members:
Dr. Garima Bajwa (supervisor, committee chair), Dr. Abedalrhman Alkhateeb, Dr. Thangarajah Akilan (Software Engineering), Dr. Dominique Cava (Thunder Bay Regional Health Research Institute)
Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.
Please join the Department of Computer Science for the upcoming thesis defense:
Presenter: Junjun Hu
Thesis title: Leakage-Aware Sentiment and Multimodal Stock-Index Analysis
Abstract: Short-horizon stock-index forecasting remains difficult because returns are noisy, non-stationary, and only partly explained by historical prices and public news. This thesis investigates whether financial-news sentiment and multimodal market representations improve three-class direction classification, while asking when high accuracy reflects genuine forecasting rather than target construction. To address these questions, it develops a staged, leakage-aware experimental framework. A finance-domain language model first transforms dated headlines into lagged daily sentiment features, which are combined with market, technical, and candlestick variables in a TFT-inspired temporal classifier for five-day future returns. The study then evaluates a smoother EMA-50 trend-state task and reconstructs a published ViT–TFT–HOG design that fuses numerical sequences, candlestick images, visual descriptors, and candle geometry across multiple stock indices. Chronological splits, simple baselines, feature ablations, per-market diagnostics, and matched target controls are used throughout. The results show that sentiment is classified reliably at the text level but adds only a weak incremental signal to future-return prediction and does not yield consistent trading value. The EMA-50 task is substantially easier because its labels are smoother and more persistent. The reconstructed multimodal model achieves stable accuracy above 93% for the reported market-state task and transfers well to additional indices. However, a deterministic audit reveals that the prices defining this target are already available in the input window; when the endpoint is moved into the genuine future, performance falls close to chance. These findings show that high classification accuracy can measure state reconstruction rather than forecasting skill. The main contribution is a reproducible, target-explicit evaluation that preserves strong reconstruction results without overstating future-return predictability.
Committee Members:
Dr. Abdulsalam Yassine (supervisor, committee chair), Dr. Abedalrhman Alkhateeb, Dr. Yong Deng (Software Engineering)
Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.
