I am a Ph.D. candidate in the Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, supervised by Prof. Ling Shi (IEEE Fellow). I received my B.S. degree in Electronic and Information Engineering from Huazhong University of Science and Technology in 2020. From August 2023 to December 2023, I was a visiting student in the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, hosted by Prof. Karl Henrik Johansson (IEEE Fellow).

My Chinese name is 霍 玮.

Research Interests

• Multi-agent system
• Distributed optimization
• Event-triggered mechanism
• Adaptive control
• Privacy protection

I keep open-minded to new problem domains and look forward to academic collaboration. Email me if you’d like to discuss. Email: whuoaa@connect.ust.hk

Selected Research Projects

Privacy-preserving Algorithms Design in Multi-agent Systems (May. 2023 – present)

• Utilize Laplacian noise and the robust push-pull technique to achieve convergence and differential privacy in directed networks.
• Exploit the randomness of stochastic compression to preserve differential privacy while reducing communication costs.
• Applications: Energy management in smart grids.

Neural Network-based Controller for Non-cooperative Nonlinear Multi-agent Systems (Aug. 2022 – Jan. 2023)

• Adopt the primal-dual method for distributed variational generalized Nash equilibrium (v-GNE) seeking.
• Design an adaptive radial basis neural network based on backpropagation to estimate the unknown nonlinear dynamics.
• Design a distributed controller with the neural network-based observer.
• Prove the exponential convergence to the v-GNE with arbitrary accuracy.
• Applications: Connectivity control games in multi-vehicle systems.

Stochastic Event-triggered Mechanism in Distributed Games (Aug. 2021 – Jan. 2022)

• Design a stochastic event-triggering law for distributed Nash equilibrium seeking, balancing the tradeoff between the communication cost and the convergence property.
• Prove the exponential convergence to the exact NE and the exclusion of Zeno behavior.
• Applications: Energy-harvesting body sensor networks in Internet-of-Medical-Things (IoMT)

Network Size Estimation from Local Observations (Undergraduate Thesis, Oct. 2019 – May. 2020)

• Develop a data-driven algorithm to estimate the total number of nodes in a dynamical network using locally observed response dynamics.
• Investigate the performance of the proposed algorithm on both linear and nonlinear networks.
• Applications: Biology; Electric power systems

Industrial Projects

Ultra-Reliable and Low Latency Communications in 5G Networks (Jan. 2023 – present)

Learning for Improving Primal Heuristics of Mixed Integer Programming Problems (Jan. 2021 – Dec. 2021)

• Propose a Bi-layer Prediction-based Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for large-scale combinatorial optimization problems.
• Propose a graph convolutional network (GCN)-based problem reduction method that removes unnecessary variables and constraints to significantly reduce the required memory and time.
• Evaluate the BP-RB on representative NP-hard problems.
• Applications: Resource allocation in wireless communication networks