AI & ML Case Studies
Research and production work demonstrating capabilities in medical AI, privacy-preserving learning, and generative systems. Each study includes methodology, results, and lessons applicable to similar challenges.
High-Accuracy Brain Tumor MRI Classification
Calibration-aware CNN-Swin hybrid model achieving 99.55% accuracy on brain tumor MRI classification with proper uncertainty quantification. Published in Frontiers in Neuroscience 2026.
Federated Learning for Medical Image Classification
Privacy-preserving learning across distributed cervical cell classification datasets. Research comparing FedAvg, FedProx, FedBN, SCAFFOLD, and MOON under non-IID conditions with explainability analysis.
LLM-Based Academic Assessment Generation
Fine-tuned language models (Llama, Mistral) generating learning-outcome-aligned academic assignments with semantic evaluation, LLM-as-judge review, and human-in-the-loop workflows. Accepted ICoDT2 2025 poster.
Research Approach
Our case studies follow academic rigor: reproducible experimental design, multi-seed evaluation for statistical confidence, ablation studies isolating component contributions, and honest reporting of limitations alongside results.
This same discipline applies to production work. Evaluation frameworks, performance benchmarks, and deployment decisions are documented and testable rather than based on subjective assessment.
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