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 interested in any issues in parallel and
distributed systems or developing parallel and distributed computing
techniques for application scenarios of any kind. I have published over 190
papers in top venues such as IEEE TC, TPDS, TKDE, TCSVT, NeurIPS, SC, EuroSys, IPDPS, ICPP, HPCA, VLDB, Micro, DAC and so on. My current research is in parallel or distributed machine/deep learning (e.g. federated learning, acceleration of Graph Neural Networks training), AI for Science (e.g., Improving training performance for Physics-Informed Neural Networks), cluster, Cloud and edge computing (e.g., optimising workload and resource management solutions), parallelized/distributed data analytical methods (e.g., anomaly detection for time series data, deep learning methods for point clouds, pattern discovery for big data), miscellaneous issues in parallel and distributed systems (e.g., optimized communication schemes in distributed systems, security-restrained high performance computing).
I always look for motivated PhD or MSc-by-research students who have
the interests in doing research in the above areas. 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
- 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
Selected Publications
- 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, 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
- Xuan Huang
- Weitong Liao
- Da Yan
- Stephen Xu
- Ashley Au
- Xiao Qin
- Kaiwen Zuo
- Yiming Wen
- Yan Qian
- Zhihao Dai
- Xuanyu Liu
- Yuchen Liu
- Yi Su
- Peng Jiang
- 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)