Professor Xiaodong Li
Professor
Details
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College: School of Computing Technologies
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Department: School of Computing Technologies
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Campus: City Campus Australia
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xiaodong.li@rmit.edu.au
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ORCID: 0000-0003-0346-1526
Open to
- Masters Research or PhD student supervision
About
Xiaodong Li received his Ph.D. degree in artificial intelligence from University of Otago, Dunedin, New Zealand. His research interests include machine learning, evolutionary computation, multiobjective optimization, multimodal optimization (niching), swarm intelligence, data mining/analytics, deep learning, journey planning, and math-heuristic methods for optimization. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award". He is an IEEE Fellow. He is also a member of ARC (Australian Research Council) College of Experts (2023–2025).
Supervisor projects
- Knowledge-Infused Deep Graph Learning for Efficient Anomaly Detection
- 22 Aug 2024
- Explainable machine learning for electrification of everything
- 1 Aug 2024
- Enhancing Machine Learning Model Performance through Advanced Hyperparameter and Topology Optimization Techniques
- 18 Jan 2024
- Saudi Arabia's Public Diplomacy in Enhancing its International Image (Case study of Saudi Arabia Public Diplomacy and International Image in Australia)
- 17 Aug 2023
- AI-Assisted Monitoring and Inspection of Solar Photovoltaic Power Plants Using Aerial Imagery
- 1 May 2023
- spatial-temporal graph neural network (STGNN) for traffic demand forecasting
- 3 Jan 2023
- Meta-learning based Warm Start Hyperparameter Optimization
- 30 Nov 2022
- Developing Machine Learning and Data Mining Techniques for Fuel Loss Detection at Service Stations
- 31 Aug 2022
- Defending Ethereum and the alike from crypto scams
- 1 Jun 2022
- Simultaneous Road Objects and Lane Detection Models in Autonomous Vehicles
- 13 Dec 2021
- Preference Learning for Multi-objective Optimisation Problems
- 21 Apr 2020
- Reinforcement Learning Approach to predict and adapt autonomous flight paths to unexpected conditions in a Safety Critical Workflow
- 12 Sep 2019
- Enhancing Combinatorial Optimization through Solution Prediction Using Machine Learning
- 24 Jul 2019
- Automatically Solving Generic Mixed Integer Linear Programs by a Lagrangian Metaheuristic
- 16 Apr 2018
- An Artificial Intelligence Platform for Design Optimization and Data Analysis: Application for Fire and Ventilation Problems
- 23 Jul 2015
- Merge Search: A Hybrid Meta-heuristic forSolving Constrained Optimisation Problems
- 2 Mar 2015
- Meta-Heuristics for Better Constraint Handling and Minimum Cost Flow Problems
- 2 Mar 2015
- Personalised and Uncertainty-Aware Multi-Criteria Journey Planning in Urban Transportation Network
- 3 Mar 2014
- Topology optimization using evolutionary technique.
- 3 Mar 2014
- Dynamic Difficulty Adjustment for Skill Acquisition in Games
- 4 Mar 2013
- Integrating decomposition methods with user preferences for solving many-objective optimization problems
- 16 Jul 2012
- Towards a More Efficient Use of Computational Budget in Large-Scale Black-Box Optimization
- 16 Jul 2012
Teaching interests
Supervisor interests
Artificial intelligence; learning algorithms; neural networks; connectionist learning models; evolutionary computation; genetic algorithms; parallel GA; genetic programming; artificial life; complex systems; adaptive systems; emergent behaviours; cellular automata; multi-agent simulation; intelligent agents; swarm intelligence; ant colony algorithms; percolation; self-organized criticality; phase transition.
Artificial intelligence; learning algorithms; neural networks; connectionist learning models; evolutionary computation; genetic algorithms; parallel GA; genetic programming; artificial life; complex systems; adaptive systems; emergent behaviours; cellular automata; multi-agent simulation; intelligent agents; swarm intelligence; ant colony algorithms; percolation; self-organized criticality; phase transition.
Research interests
Artificial Intelligence and Image Processing, Numerical and Computational Mathematics, Applied Mathematics, Computation Theory and Mathematics, Information Systems, Aerospace Engineering
