Zhishang Wang

Zhishang Wang, Ph.D.

Assistant Professor
Sustainable Computing Cluster
Division of Computer Engineering
Department of Computer Science and Engineering
The University of Aizu

Email: zwang@u-aizu.ac.jp
Tel.: +81 0242-37-2575 (Int. 3204 / 3225)

[Openings]: I am looking for highly self-motivated and self-disciplined M.S. students who are strongly committed to the design, assembly, and coordination of android systems operating in mission-critical environments (e.g., disaster response).
Students will explore the internal structure of androids (frames, sensors, actuators), assemble working units, and implement essential functions such as vision and mobility. The research also involves developing distributed task allocation algorithms that allow androids to make autonomous decisions while collaborating as a team.
If you are interested, please feel free to drop me an email.

Research

Distributed Android Systems for Mission-Critical Assistance (Link)

An android is an artificial being with a human-like body and appearance, designed to closely mimic real humans. As android deployment expands into mission-critical settings such as disaster response and emergency support, coordination becomes challenging due to weak communication signals, limited battery capacity, heterogeneous capabilities, and rapidly changing task requirements. This research focuses on the end-to-end design, assembly, and coordination of multi-android systems operating under such constraints. Students will investigate android internal structures (body frames, sensors, actuators, and control modules), assemble working android units, and implement essential functions including vision, audio sensing, mobility, and object manipulation. On the system side, we develop distributed decision-making and cooperation mechanisms—starting from constraint-aware task allocation and extending to multi-module sensing/control integration and team-level coordination—then evaluate performance in simulated mission scenarios to study scalability, robustness, and practical deployment readiness.


AEBiS (Link)

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

Selected Publications

Journals

Conferences

Invited Talk

Employment

Postdoctoral Researcher, The University of Aizu, April 2023 – March 2025.
AI Engineer, Aizu Computer Science Laboratories, January 2020 – March 2021.

Education

Teaching

Oral Presentation

Services

Awards

  • 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.