Zhishang Wang

Zhishang Wang

Postdoctoral Researcher

Division of Computer Engineering
Department of Computer Science and Engineering
The University of Aizu

Email: zwang@u-aizu.ac.jp
Tel.: +81 0242-37-2500 (Int. 3204)


Postdoctoral Researcher, The University of Aizu, 2023 April – now.
AI Engineer, Aizu Computer Science Laboratories, 2020 January – 2021 March.


  • Ph.D. in Computer Science and Engineering, The University of Aizu, Japan, 2023
  • M.Sc. in Computer Science, University of Freiburg, Germany, 2019
  • B.Sc. in Computer Science, Wuhan University, China, 2014
  • Research


    A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. An AI-enabled, blockchain-based electric vehicle integration system is developed for power management in a smart grid platform based on EV and solar carport. We have developed a low-power AI-chip and various software tools for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy.

    Trustworthy Multi-Blockchain-Based Collaborative Learning

    Collaborative edge learning has emerged in various domains like vehicular networks and medical care, allowing local model training on edge devices while preserving privacy. A hybrid clustered blockchain method (HCB) is proposed for collaborative edge learning, where model transmission is performed in an on-chain-merge-off-chain manner. Each cluster performs an off-chain transmission of local model updates and an on-chain distribution of global model updates, and elects a delegate node to serve as a model aggregator. The delegate nodes form a main blockchain in which the global model updates of each cluster are exchanged. A delegate-based adaptive model aggregation for robust collaborative learning called DAMA-RCL ensures high-quality model selection and aggregation during collaborative learning. A disassembling-reassembling method is also introduced to enable practical model transmission on the blockchain network.


    Patent Pending

  • A. Ben Abdallah, Zhishang Wang, Masayuki Hisada, ”An electricity trading system and an electricity trading method [電力取引システム及び電力取引方法に関する], 特願2022-022472
  • A. Ben Abdallah, Zhishang Wang, Khanh N. Dang, Masayuki Hisada, ”EV Power Consumption Prediction Method and System for Power Management in Smart Grid [スマートグリッドにおける電力管理のためのEV消費電力予測方法とシステム]”, 特願2023-020162
  • Selected Publications

    Oral Presentation



  • Outstanding Research Assistant (AY2021), The University of Aizu.
  • Best Presentation Award. Postgraduate Forum of ACM International Conference on Research in Adaptive and Convergent Systems 2022, Aizuwakamatsu, Japan. October 3-6, 2022.