Books

  • 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)

  • Liang Wang, Jianxin Zhao. Strategic Blueprint for Enterprise Analytics: Integrating Advanced Analytics into Data-Driven Business. First edition, Springer Cham, 2024 April. (link).

Journals

  • 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]

Conferences

  • J. Zhao, M-B. Lin, A. Vinel. “V2X-Based Decentralized Singular Value Decomposition in Dynamic Vehicular Environment.” 1st Workshop on Cooperative Intelligence for Embodied AI, ECCV 2024. [link]

  • Wang Y, Wu J, Hua X, Liu CH, Li G, Zhao J, Yuan Y, Wang G. “Air-ground spatial crowdsourcing with uav carriers by geometric graph convolutional multi-agent deep reinforcement learning.” 2023 IEEE 39th International Conference on Data Engineering (ICDE) 2023 Apr 3 (pp. 1790-1802). IEEE. [paper]

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

  • 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, 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]

Teaching

  • 2024.4-8: Collective Perception in Autonomous Driving, Master level course. Teaching basic fundamental ML knowledge for autonomous driving, including ML, DL, object detection, Bayesian Inference, etc.

Supervision

  • 2024 @ KIT: one master student on the topic of Pedestrian Intention Prediction

  • 2021-2023 @ BIT : two master students on the topic of federated learning, and one master student on the topic of optimization of deep learning model compilation.

  • 2016 @ Cambridge: Computer Networking, undergraduate students

Patents

  • A Large-Scale Edge Machine Learning Training Method Based on Probabilistic Sampling. Jianxin Zhao, Rui Han, Chi Liu. CN Patent No. ZL202110285186.X; Nov.8, 2022, China National Intellectual Property Administration.

  • A client selection method for edge-side federated learning under heterogeneous data. Jianxin Zhao, Chi Liu, Yanhao Feng, Xinyu Chang. CN Patent No. ZL20211498897.1; May.31, 2024, China National Intellectual Property Administration.

Other

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