PAUL BAKAKI

PAUL BAKAKI is a Lecturer in Computer Science with research focused on interdisciplinary applications of computational methods in medical imaging and traffic dynamics. His work integrates graph theory, shape analysis, and machine learning to address challenges in surgical outcome assessment and transportation systems. He is affiliated with the Centre for Intelligent Visual Computing and Complex Systems Research Centre.

Education:

• PhD in Computer Science (2024) – Thesis: 'Computational Approach for an Automatic Facial Appearance Outcome Measure of Cleft Lip Surgical Repair Using Digital Images'

Research Interests: His primary areas include:
- Development of computational tools for medical aesthetics evaluation
- High-order graph models for traffic system analysis
- Application of shape analysis in surgical outcomes assessment
- Expander graphs and their dynamic modeling capabilities
- Image processing techniques for partially occluded facial reconstructions

Research Trends: His publications highlight a dual focus on medical imaging applications (e.g., cleft lip repair assessment) and traffic engineering innovations using advanced graph theory. The 2024 work pioneers high-order evolving graphs for traffic dynamics, while the 2021-2022 studies establish novel methods for automated surgical outcome evaluation.

Labs/Teams: Active in the Centre for Intelligent Visual Computing Research and Complex Systems Research Centre, collaborating on projects blending computational models with real-world clinical and transportation challenges.