Research Areas & Expertise
Key Research Themes
1. Decision Support Systems for Transportation
Designing interactive, data-driven tools that help transportation planners and operators make better decisions under uncertainty. Applications include maintenance scheduling, vehicle routing, traffic signal optimization, and network planning.
Key methods: Multi-objective optimisation, evolutionary algorithms, discrete event simulation, interactive visualization
Example work: Railway rolling stock maintenance scheduling, intelligent vehicle routing systems
2. Optimisation & Operations Research
Developing efficient algorithms for complex combinatorial problems where multiple objectives must be balanced (cost, time, reliability, safety). Emphasis on real-world constraints and computational feasibility.
Key methods: Evolutionary algorithms, metaheuristics, constraint programming, multi-objective optimization (NSGA-II, MOEA/D)
Example work: Maintenance plan optimization, shift scheduling, vehicle fleet management
3. Reinforcement Learning for Intelligent Transportation Systems
Applying RL techniques to teach autonomous or semi-autonomous systems to optimize transportation workflows. Focus on learning from data and adapting to dynamic environments.
Key methods: Q-learning, policy gradient methods, deep reinforcement learning, multi-agent RL
Example work: Adaptive traffic signal control, dynamic vehicle routing, congestion management
4. Sports Analytics & Human Performance Enhancement
Leveraging biomechanical analysis, force plate data, video analytics, and ML to quantify athlete performance, identify injury risk, and guide coaching decisions. Applied through head coaching and collaboration with sports science teams.
Key methods: Biomechanical analysis, computer vision, time-series analysis, classification/regression models
Example work: Player performance metrics, injury risk assessment, training load analysis, tactical video analysis
5. Computer Vision & Image/Video Analytics
Extracting structured data from visual media for decision support. Applications span both transportation (traffic monitoring, vehicle detection) and sport (player tracking, technique analysis).
Key methods: Object detection, pose estimation, optical flow, CNN-based feature extraction
Example work: Video-based performance monitoring, traffic flow estimation, coaching analytics
Research Impact
- Published research: 7 peer-reviewed publications in top-tier venues (IEEE, journals in operations research and AI)
- Industry collaboration: Active partnerships with transportation operators, railway operators, and sports organizations
- Tools & systems: Developed and deployed decision support tools that optimize real-world operations
- Speaking & teaching: 6 invited talks and presentations at academic and industry forums
Methodological Strengths
- Problem formulation: Translating real-world challenges into mathematical models and data science tasks
- Algorithm design: Designing efficient, scalable solutions for constrained optimization and learning problems
- Implementation: Building practical tools and systems, not just theoretical work
- Collaboration: Working effectively with domain experts, industrial partners, and interdisciplinary teams
- Communication: Presenting complex technical work clearly to both academic and non-technical audiences
Browse publications and talks for specific papers, projects, and speaking engagements.