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Ligang He

Research interests

I am interested in doing research on any issues in Parallel and Distributed systems or developing parallel and distributed computing techniques for any application scenarios. I have published over 180 papers in top venues such as IEEE TC, TPDS, TKDE, TCSVT, SC, IPDPS, ICPP, HPCA, VLDB and so on. My current research is in parallelized or distributed machine/deep learning (e.g. federated learning, acceleration of Graph Neural Networks training), 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 (e.g., communication schemes and security) in parallel and distributed systems.

I always look for motivated PhD or MSc-by-research students who have the interests in doing research in above areas.

Research highlight

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

  • 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
  • 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
  • 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

Office: CS2.05
Phone: +44 2476573802
Email: ligang.he AT

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