Scholar

Xiaodong Li, IEEE Fellow

Google Scholar ID: AQewL04AAAAJ

Professor, School of Computing Technologies, RMIT University, Melbourne

Artificial IntelligenceMachine LearningEvolutionary ComputationLarge-Scale OptimizationSwarm Intelligence

Citations & Impact

All-time

Citations

9,259

H-index

i10-index

124

Publications

Co-authors

list available

Contact

Publications

20 items

Browse publications on Google Scholar (top-right) ↗

Resume (English only)

Academic Achievements

Recipient of the 2013 ACM SIGEVO Impact Award
2017 IEEE CIS Outstanding Paper Award for IEEE Transactions on Evolutionary Computation
Winner of the IEEE CEC 2019 Large-Scale Global Optimization (LSGO) competition with the RDG3 algorithm
Elected IEEE Fellow in 2020 'for contributions to large-scale and particle swarm optimization'
Google Scholar h-index: 65
Secured multiple competitive national research grants:
- ARC Discovery Grant (DP250103251): 'Learning to Value Constraints', AUD $563K (2025–2027)
- ARC Industrial Transformation Research Hubs (IH240100009): Intelligent Energy Efficiency in Future Protected Cropping, AUD $5M (2024–2029)
- ARC Linkage Grant (LP230100439): 'Explainable machine learning for electrification of everything', AUD $512K (2024–2026)
- CSIRO Next Generation AI Program 2022: 'All System analysis – analytics and intelligent automation', AUD $1,073K (2022–2025)

Background

Professor in Artificial Intelligence, School of Computing Technologies, RMIT University, Melbourne, Australia
Assistant Associate Dean of the Data Science and Artificial Intelligence (DSAI) discipline
IEEE Fellow (since 2020)
IEEE CIS Distinguished Lecturer (2024–2026)
Member of the Australian Research Council (ARC) College of Experts (2023–2026)
Leader of the ECML (Evolutionary Computation and Machine Learning) research group
Research interests include: machine learning, evolutionary computation, multiobjective optimization, multimodal optimization (niching), swarm intelligence, data mining/analytics, deep learning, journey planning, math-heuristic methods for optimization, large language models, explainable AI, reinforcement learning, game theory, etc.

Co-authors

60 total