• Liang Wang, Jianxin Zhao, and Richard Mortier. OCaml Scientific Computing: Functional Programming in Data Science and Artificial Intelligence. First edition, Springer International Publishing, 2022 April. Series ISSN: 1863-7310. DOI: 10.1007/978-3-030-97644-6. (link)

  • Liang Wang and Jianxin Zhao. Architecture of Advanced Numerical Analysis Systems - Designing a Scientific Computing System using OCaml. First edition, Apress Berkeley, CA, 2022 Dec. Softcover ISBN: 978-1-4842-8852-8. Licensed under CC BY. (link)


  • Jianxin Zhao, Rui Han, Yongkai Yang*, Benjamin Catterall, Chi Harold Liu, Lydia Y. Chen, Richard Mortier, Jon Crowcroft, and Liang Wang, “Federated Learning with Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge,” IEEE Transactions on Services Computing,2021. DOI: 10.1109/TSC.2021.3109910.

  • Jianxin Zhao, Pierre Vandenhove, Peng Xu , Hao Tao, Liang Wang, Chi Harold Liu, and Jon Crowcroft, “Parallel and Memory-Efficient Distributed Edge Learning in B5G IoT Networks,” IEEE Journal of Selected Topics in Signal Processing, 2022. DOI: 10.1109/JSTSP.2022.3223759

  • Jianxin Zhao, Xinyu Chang, Yanhao Feng, Chi Harold Liu, and Ningbo Liu*, “Participant Selection for Federated Learning with Heterogeneous Data in Intelligent Transport System,” IEEE Transactions on Intelligent Transportation Systems, 2022. DOI: 10.1109/TITS.2022.3149753

  • Jianxin Zhao, Yanhao Feng, Xinyu Chang, Peng Xu, Shilin Li, Chi Harold Liu, Wenke Yu, Jian Tang, and Jon Crowcroft, “Energy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning, ” IEEE Transactions on Network Science and Engineering, 2022. DOI: 10.1109/TNSE.2022.3185672

  • Jianxin Zhao*, Yanhao Feng, Xinyu Chang, and Chi Harold Liu, “Energy-efficient client selection in federated learning with heterogeneous data on edge,” Peer-to-Peer Networking and Applications,2022. DOI: 10.1007/ s12083-021-01254-8.

  • Gao, H., Liu, C.H., Wang, W., Zhao, J., Song, Z., Su, X., Crowcroft, J. and Leung, K.K., 2015. A survey of incentive mechanisms for participatory sensing. IEEE Communications Surveys & Tutorials, 17(2), pp.918-943. [paper]

  • Zhao, J., Liu, C. H., Chen, M., Liu, X., & Leung, K. K. (2015, June). Energy-efficient dynamic event detection by participatory sensing. In 2015 IEEE International Conference on Communications (ICC) (pp. 3180-3185). [paper]


  • J. Zhao, T. Tiplea, R. Mortier, J. Crowcroft, and L. Wang. “Data Analytics Service Composition and Deployment on Edge Devices.” Big-DAMA workshop, SIGCOMM’18 [paper]

  • J. Zhao. “Executing Owl Computation on GPU and TPU.” OCaml 2019. [talk]

  • J. Zhao, R. Mortier, J. Crowcroft, and L. Wang. “Privacy-preserving Machine Learning Based Personal Data Analytics System.” AAAI/ACM conference on Artificial Intelligence, Ethics, and Society (AIES’18) [paper]

  • J. Zhao, R. Mortier, J. Crowcroft, and L. Wang. “User-centric Composable Services for Personal Data Analytics.” SOSP’17 [paper]

  • J. Zhao. “Towards Security in Distributed Home System.” EuroSys’17 Doctoral Workshop [paper] [slides]

  • Richard Moriter et. al. “Personal Data Management with the Databox: What’s Inside the Box?” CAN’16 [paper]


  • J. Zhao. “Optimisation of a Modern Numerical Library: a Bottom-Up Approach”. PhD Thesis. 2020.