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)

    This book covers a wide range of topics in scientific computing:

    • Part I introduces basic numerical techniques, including statistics, linear algebra, ordinary differential equations, and signal processing.
    • Part II shows advanced numerical techniques: algorithmic differentiation, optimization and regression, and neural network.
    • Part III includes a range of computer vision case studies.

  • 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)

    • Based on our hands-on experience in developing the Owl library, this book aims to present the architecture design and optimization of various core components in a modern numerical library.

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

    • This book is a comprehensive guide for professionals, leaders, and academics seeking to unlock the power of data and analytics in the modern business. It delves into the strategic, architectural, and managerial aspects of implementing enterprise analytics systems in large enterprises.

  • Introduction to Internet of Thing Technologies. Chi Harold Liu (Editor-in-Chief), Rui Han, Jianxin Zhao, and Jian Ma (Associate Editor-in-Chief), 3rd edition, China Machine Press, 2021.Nov.

Open Source Project

  • Owl - OCaml Scientific and Engineering Computing [GitHub]

    • Owl is a dedicated open-source system for scientific and engineering computing, a core numerical library in the OCaml Language community. I have worked as a core developer and now the leader of this project since its inception on 2016, gaining hands-on software development and architecting experience. The code base includes 133K LoC OCaml and 103K LoC C code. 1.3K GitHub stars.

Journals

  • 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 17, no. 1 (2022): 222-233. [paper]

  • 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 24, no. 1 (2022): 1106-1115. [paper]

  • 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 10, no. 3 (2022): 1352-1363. [Paper]

  • 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 15, no. 2 (2022): 1139-1151. [paper]

  • 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 15, no. 2 (2021): 614-626. [paper]

  • Shuang Li, Mixue Xie, Kaixiong Gong, Jianxin Zhao*, Chi Harold Liu, and Guoren Wang, “End-to-End Transferable Anomaly Detection via Multi-spectral Cross-domain Representation Alignment,” IEEE Transactions on Knowledge and Data Engineering, October 2021. DOI: 10.1109/TKDE.2021. 3118111. [paper]

  • 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]

  • Ye, Y., Liu, C.H., Dai, Z., Zhao, J., Yuan, Y., Wang, G. and Tang, J., 2023, April. Exploring both individuality and cooperation for air-ground spatial crowdsourcing by multi-agent deep reinforcement learning. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) (pp. 205-217). 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]

Funding

  • Connecting Young Scientists at KIT. Total grant: €5K. 2024. This program support a short-term journeys to a Germany vehicle manufactures such as BMW and Volkswagen for deeper understanding of real-world application of technologies in the industry and building networks.

  • Funding to Support Maintenance of Owl. OCaml Software Foundation. Total grant: €10K. 2024

  • Fundamental Theories and Technologies for Edge-Side Big Data Computing, Key Project of the National Natural Science Foundation of China (NSFC), €380K. Core member. 2022.

  • Key Technologies for Edge Intelligent Perception and Capability Enhancement, Key Project Supported by the Joint Funds project of the National Natural Science Foundation of China (NSFC), €332K. Core member. 2022.

  • A Big Data Lake System for Heterogeneous Industrial Internet Systems, Key Project of the National Key R&D Program, €460K. Core member, 2021.

  • Funding for open access publication of book “Architecture of Advanced Numerical Analysis Systems”, OCaml Software Foundation, $15K.

  • Postdoctoral International Exchange Program Scholarship, €51K. 2021.

  • Databox: Privacy-aware Infrastructure for Managing Personal Data. EPSRC (EP/N028260/1), £1.24 Million. Main researcher. 2016.

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.

Teaching

  • 2024/2025.4-8: Collective Perception in Autonomous Driving, Master level course. Teaching basic fundamental ML knowledge for autonomous driving, including ML, DL, object detection, Bayesian Inference, etc.

  • 2024.10-2025.2: Artificial Intelligence for Autonomous Driving，Master level Seminar.

  • 2016-2017. Teaching Assistant at the Milton Road Primary School, Cambridge. The STIMULUS Program is a community service program which gives Cambridge University students the opportunity to work with pupils in local schools, helping with Maths, Science, Computing or Technology lessons.

Supervision

  • 2024 @ KIT: two master students on the topic of pedestrian intention prediction and BFT method’s application in v2x/vehicle-based federated learning

  • 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.

  • 2018-2020 @ Cambridge: two master students on the topic of deep learning computation optimization

  • 2016 @ Cambridge: Computer Networking, undergraduate students

Other

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