My main interests concentrate on deep (reinforcement) learning, combinatorial optimization and integer programming. I am currently devoted to the learning for optimization field, especially the neural combinatorial optimization topic, with the aim to explore deep learning based methods to solve combinatorial optimization problems, including vehicle routing, airport ground handling, on-demand delivery with UAV, and integer programs. It is promising to exploit deep learning in real-world optimization and decision making, with the power of data.
- Artificial Intelligence: Deep Reinforcement Learning, Graph Neural Network, VAEs & GANs
- Operations Research: Heuristic Search, (Stochastic) Integer Programming, Multi-Objective Optimization
- Applications: Transportation, Scheduling, Airport Ground Handling, On-demand Delivery
Make the world a better place by learning and optimizing
Yaoxin Wu received the B.Eng degree in traffic engineering from Wuyi University, Jiangmen, China, in 2015, the M.Eng degree in control engineering from Guangdong University of Technology, Guangzhou, China, in 2018, and the Ph.D. degree in computer science from Nanyang Technological University, Singapore, in 2023. He was a Research Associate with the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU). He joins the Department of Information Systems, Faculty of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, as an Assistant Professor. His research interests include deep learning, combinatorial optimization and integer programming.
Deep Reinforcement Learning for UAV Routing in the Presence of Multiple Charging StationsIEEE Transactions on Vehicular Technology (2023)
A Review on Learning to Solve Combinatorial Optimisation Problems in ManufacturingIET Collaborative Intelligent Manufacturing (2023)
Learning Improvement Heuristics for Solving Routing ProblemsIEEE Transactions on Neural Networks and Learning Systems (2022)
Graph Learning Assisted Multi-Objective Integer Programming(2022)
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs(2022)
Current Educational Activities
No ancillary activities