Ligang He
Research interests
I am a Full Professor of Computer Science. My research focuses on the scalability, performance and resource efficiency of parallel and distributed systems, particularly their two-way integration with AI: using parallel and distributed computing to enable scalable AI, and using AI to advance high-performance scientific computing.
My work has evolved from performance modelling, scheduling and resource management in cluster, grid and cloud environments to distributed machine learning, acceleration of AI training and inference, and AI-enabled high-performance scientific computing. My current main research interests include:
- Distributed machine learning, including federated learning and learning across different types of distributed environments;
- Parallel and distributed techniques for accelerating the training and inference of machine-learning models, including LLMs and graph neural networks;
- AI for Science and scientific machine learning, including optimisation of physics-informed neural networks and neural operators;
I have published more than 200 papers in leading journals and conferences, including IEEE TC, IEEE TPDS, IEEE TKDE, IEEE TCSVT, NeurIPS, SC, EuroSys, IPDPS, ICPP, HPCA, VLDB, MICRO and DAC.
I welcome enquiries from prospective PhD and MSc-by-Research students interested in parallel and distributed computing, distributed AI, scalable machine learning and AI-enabled scientific computing. Please feel free to contact me.
Research Highlight
- Top 2% Scientists worldwide in the field of Distributed Computing, as per composite indicators compiled by Stanford and Elsevier in 2024 and 2025
- SustainAIRA6G: Energy-Efficient Sustainable AI-driven Resource Allocation for 6G-empowered Edge-Fog-Cloud Continuum, funded by EPSRC, Warwick PI, 2024
- Developing Adaptive Federated Learning Frameworks for Heterogenous and Dynamic Electronic Health Records, The UK-Saudi Challenge Fund, funded by British Council, PI, 2024
- National Edge AI Hub for Real Data: Edge Intelligence for Cyber-disturbances and Data Quality, funded by EPSRC, co-I, 2024
- Two MSc dissertation projects that I supervised in 2022/23 and 2024/25 respectively won the Best MSc Dissertation Awards in the department
- The paper“SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead”is the runner-up of the 2021 Best Paper Award for IEEE Transactions on Computers
- DepGraph (collaborated with Huazhong University of Science and Technology and published in HPCA-2021) is ranked No. 2 in the Big Data category in the November 2021 ranking table of Green Graph 500, and ranked No. 3 in SSSP (single-source shortest paths) performance in the November 2021 ranking table of Graph 500
Selected Publications
- Y. Liu, L. He, Z. Zheng and S. Ren, "PFed-NS: an Adaptive Personalized Federated Learning Scheme through Neural Network Segmentation" in IEEE Transactions on Computers, vol. , no. 01, 2025, pp. 1-13, PrePrints 5555, doi: 10.1109/TC.2025.3547138
- H. Yu, Y. Zhang, L. He, Y. Zhao, X. Li, R. Xin, J. Zhao, X. Liao, H. Liu, B. He, H. Jin, "RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network", The 57th IEEE/ACM International Symposium on Microarchitecture (Micro'57), Nov 2-6, 2024, Texas, USA
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Z. Dai, L. He, S. Yang, M. Leeke, "SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series", The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS2024), 2024
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D. Yan, L. He, "DP-PINN: A Dual-Phase Training Scheme for Improving the Performance of Physics-Informed Neural Networks", the 24th International Conference on Computational Science, 2024 (The extension of this paper has been invited to submit to the special issue of Journal of Computational Science
- L. Li, L. He, J. Gao, and X. Han (2022) "PSNet : fast data structuring for hierarchical deep learning on point cloud". IEEE Transactions on Circuits and Systems for Video Technology,2022, doi:10.1109/TCSVT.2022.3171968
- Wu, L. He, W. Lin, Y. Su, Y. Cui, C. Maple, S. Jarvis, "Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality", in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 9, pp. 4147-4160, 1 Sept. 2022, doi: 10.1109/TKDE.2020.3035685
- Wu, L. He, W. Lin, R. Mao, "Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems", IEEE Transactions on Parallel and Distributed Systems, Vol.32, no.7, pp.1539-1551, 2021
- Wu, L. He, W. Lin, R. Mao, C. Maple, S. Jarvis, "SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead", IEEE Transactions on Computers, vol. 70, pp. 655-668, 2020, DOI: 10.1109/TC.2020.2994391
- Zhang, X. LIAO, H. Jin, L. He, B. He, H. Liu, L. Gu, "DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph Processing", The 27th IEEE International Symposium on High-Performance Computer Architecture (HPCA-2021), 2021
- J. Li, L. He, S. Ren, R. Mao, "Developing a Loss Prediction-based Asynchronous Stochastic Gradient Descent Algorithm for Distributed Training of Deep Neural Networks", Proceedings of the 49th International Conference on Parallel Processing(ICPP2020), 2020
