Academic papers and theses exploring machine learning, optimization, and applied mathematics.
Proposes Reinforced Linear Genetic Programming (RLGP), a novel hybrid that layers Q-Learning on top of LGP to learn optimal register-action assignments — eliminating manual domain-specific mapping. Evaluated on OpenAI Gym CartPole-v1 and MountainCar-v0 using a custom Rust framework. LGP achieved mean reward 454 on CartPole; RLGP solved the task but plateaued at 213, revealing a fundamental tension between evolutionary instability and RL convergence. Published on arXiv (2601.09736) and archived at Dalhousie University.
Examines methods to improve CNN performance without large datasets or specialized hardware. Trained on just 85 web-scraped images of Eastern cottontail rabbits and European hares, using Stratified K-Fold Cross Validation to handle the small, unbalanced dataset. Best configuration achieved 0.647 test accuracy (F1 0.575, precision 0.8, recall 0.625) with SGD+momentum and batch size 2. Demonstrates that even with optimal architecture choices and dropout regularization, data quantity remains the fundamental bottleneck.
Develops a CNN for medical self-diagnosis that returns the most probable condition from natural language symptom descriptions. Compares One-Hot Encoding (56x4210 word-stem matrix with Witten-Bell synthetic augmentation) against unsupervised FastText embeddings on data scraped from Mayo Clinic, UpToDate, Healthline, and NHS. The One-Hot CNN achieved 93% accuracy and 90% recall with perfect precision on migraines and tetanus; FastText reached only 73% accuracy, demonstrating that sparse representations outperform learned embeddings on small medical corpora.