Contact
Department of Computer Science
University of Warwick
Coventry, CV4 7AL
United Kingdom
Office: CS2.05
Phone: +44 2476573802
Email: ligang.he AT warwick.ac.uk
Research
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Research Interests
I am a Full Professor of Computer Science specialising in parallel and distributed computing.
My research focuses on the scalability, performance and resource efficiency of parallel and
distributed systems, particularly their two-way integration with artificial intelligence:
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:
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Distributed machine learning, including federated learning and learning across
different types of distributed environments;
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Parallel and distributed techniques for accelerating the training and inference of
machine-learning models, including LLMs and graph neural networks;
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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.
Latest Highlights
- 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
- The MSc dissertation project I supervised in the 2022/23 academic year, titled "Developing a Resource Discovery Framework in a Network of Mobile Devices", won the Best MSc Dissertation Award in the department
- Our paper in federated learning, "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 (the graph processing framework, collaborated with Huazhong University of Science and
Technology; the paper is published in HPCA-2021) is ranked 2nd in the Big
Data category in the November 2021 ranking table of Green Graph 500,
and ranked 3rd in SSSP (Single-Source Shortest Paths) performance
in the November 2021 ranking table of Graph 500
Highlighted 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
- Qi, Hao, Luo, Kang, He, Ligang, Zhang, Yu, Cai, Minzhi, Dai, Jingxin, He, Bingsheng, Jin, Hai, Zhang, Zhan, Zhao, Jin, Yue, Hengshan, Yu, Hui and Liao, Xiaofei (2025) OHMiner : An overlap-centric system for efficient hypergraph pattern mining. In: 2025 ACM European Conference on Computer Systems (EuroSys2025), Rotterdam, Netherlands, 30 Mar - 3 Apr 2025.
- 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
- 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), 2004
- 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, "PSNet: fast data structuring
for hierareep learning on point cloud". IEEE Transactions on
Circuits and Systems for Video Technology,2022, doi:10.1109/TCSVT.2022.3171968
- W. 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
- W. Wu, L. He, W. Lin and C. Maple, "FedProf: Selective Federated Learning Based on Distributional Representation Profiling," in IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 6, pp. 1942-1953, June 2023
- W. 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
- W. Wu, L. He, W. Lin, , 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.2091
- J. Zhao, Y. Zhang, X. Liao, L. He, B. He, H. Jin and H. Liu, "LCCG: a
locality-centric hardware accelerator for high throughput of concurrent
graph processing", Proceedings of the International Conference for High
Performance Computing, Networking, Storage and Analysis (SC '21), 2021
- Y. 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
- 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
PhD Students
- Yuanzhen Shuai
- Yijie Yang
- Delong Huang
- Tim Wang (MSc by Research)
- Xuan Huang
- Weitong Liao
- Da Yan
- Stephen Xu
- Ashley Au
- Xiao Qin
- Yan Qian
- Xuanyu Liu
- Yuchen Liu
- Zhihao Dai (graduated)
- Yi Su (graduated)
- Hao Wu (graduated)
- Yujue Zhou (graduated)
- Zhiyan Chen (graduated)
- Junyu Li (Graduated)
- Wentai Wu (Graduated)
- Mohammed Alghamdi (Graduated)
- Shenyuan Ren (Graduated)
- Zhuoer Gu (Graduated)
- Nadeem Chaudhary (Graduate)
- Chao Chen (Graduated)
- Huanzhou Zhu (Graduated)
- Bo Gao (Graduated)