HighlightsThese highlights are AI-generated using the faculty's CV and Google Scholar profile
CAO Zhiguang is an internationally recognized expert in neural combinatorial optimization, reinforcement learning, and intelligent transportation systems, with impactful contributions to both foundational theory and real-world logistics applications.
Bridges methodological innovation in neural combinatorial optimization and reinforcement learning with practical impact in logistics, transportation, and manufacturing; recognized for advancing generalizable, scalable, and interpretable AI models; recipient of multiple international awards including IEEE Outstanding Paper Award and World's Top 2% Scientist; strong leadership in academic service and editorial roles; active in grant-funded research and intellectual property development.
Focused research areas include Development of robust neural and learning-based solvers for combinatorial optimization, especially vehicle routing and scheduling problems; foundation models for logistics and transportation; scalable reinforcement learning for dynamic and stochastic environments; generalizable AI for real-world operations; hybrid and multimodal approaches integrating graph, image, and sequence data.
Bridges methodological innovation in neural combinatorial optimization and reinforcement learning with practical impact in logistics, transportation, and manufacturing; recognized for advancing generalizable, scalable, and interpretable AI models; recipient of multiple international awards including IEEE Outstanding Paper Award and World's Top 2% Scientist; strong leadership in academic service and editorial roles; active in grant-funded research and intellectual property development.
Focused research areas include Development of robust neural and learning-based solvers for combinatorial optimization, especially vehicle routing and scheduling problems; foundation models for logistics and transportation; scalable reinforcement learning for dynamic and stochastic environments; generalizable AI for real-world operations; hybrid and multimodal approaches integrating graph, image, and sequence data.
Areas of Expertise
Learning to OptimizeNeural Combinatorial OptimizationAI for Optimization
Past Awarded Grant
- Learning Assisted Human-AI Collaboration for Large-scale Practical Combinatorial Optimization, Research Programme, AI Singapore , Co-PI (Project Level): CAO Zhiguang, 2024, S$4,435,990
- Learning Robust Neural Heuristic for Solving Vehicle Routing Problems in Logistics, SMU Internal Grant, Ministry of Education (MOE) Tier 1 , PI (Project Level): CAO Zhiguang, 2023, S$107,300
- Towards Generalizable Deep Models for Solving Vehicle Routing Problems in Logistics, A*STAR Career Development Fund, Agency for Science, Technology and Research (A*STAR) PI (Project Level): CAO Zhiguang, 2022, SGD142,000
- The Air Cargo Load Planning and Break-down Problem, ST Engineering - NTU Corporate Lab, SINGAPORE TECHNOLOGIES LAND SYSTEMS LTD Co-PI (Project Level): CAO Zhiguang, 2018, SGD870,000
Latest Publications
Showing up to 6 latest publications from the past 5 years.
- N Zhang, Z Cao, J Zhou, C Zhang, YS OngInternational Conference on Learning Representations (ICLR), 2026
- R Zhu, C Zhang, Z CaoInternational Conference on Learning Representations (ICLR), 2026
- H Yi, Z Huang, Y Ma, Z CaoInternational Conference on Learning Representations (ICLR), 2026
- S Gui, S Liu, X Wang, Z CaoInternational Conference on Learning Representations (ICLR), 2026
- X Xiao, C Zhang, W Song, Z CaoInternational Conference on Learning Representations (ICLR), 2026
- J Bi, Z Cao, J Zhou, W Song, Y Wu, J Zhang, Y Ma, C WuInternational Conference on Learning Representations (ICLR), 2026
Qualification
- PhD, Nanyang Technological University, 2017
Teaching Topics
- Statistical Thinking for Data Science