Prior to joining SIAT, I worked as a research and teaching assistant at the Research Institute of Trustworthy Autonomous Systems (RITAS) and the Department of Computer Science and Engineering (CSE) at the Southern University of Science and Technology (SUSTech) in Shenzhen, China, from July 2021 to September 2023.

I received my Ph.D. in Computer Science and Technology from Beijing Institute of Technology (BIT), Beijing, China, in 2023. Both of my Bachelor and Masters in Engineering (B.E. and M.E.) were from Northwestern Polytechnical University (NPU), Xi’an, Shaanxi Province, China. I was a recipient of the Chinese Government Scholarship awarded by Chinese Scholarship Council (CSC).

My research interests span Decentralized Finance (DeFi), Blockchain, Generative AI (GenAI), Multi-access Edge Computing (MEC), Network Slicing, Software-Defined Networking (SDN), and Information-Centric Networking (ICN).

💻Research Portfolio

💼PostDoc@SIAT (2024 to Ongoing)

My current research is steadily advancing into the intersections of Blockchain, DeFi, and GenAI. More specifically, I am focusing on the following research directions:

• Decentralized Finance (DeFi): I am exploring the underlying architecture of mainstream blockchains such as Ethereum and Bitcoin to enhance their core functionalities for building decentralized financial ecosystems. By introducing new methodologies, I aim to address existing limitations in interoperability, scalability, and user engagement. This will promote the widespread adoption and practical application of DeFi. My research focuses on ensuring the integrity and fairness of transactions within blockchain networks where I am designing frameworks to address and mitigate potential exploitative behaviors, such as Maximal Extractable Value (MEV) activities. Through detailed analysis of transaction dynamics, my objective is to propose comprehensive strategies to effectively protect DeFi transactions from manipulation risks. This will help build a more equitable, stable, and resilient financial ecosystem.

• Generative AI and Large Models: I am investigating the frontier of generative AI (GenAI) and large models, including Large Language Models (LLMs) and Large Multimodal Models (LMMs). My focus is on leveraging GenAI to solve complex problems in blockchain and DeFi ecosystems. By integrating GenAI technology, I aim to enhance the semantic understanding and contextual analysis of content in blockchain transactions, smart contracts, and Non-Fungible Tokens (NFTs). Through this interdisciplinary effort, my objective is to significantly improve the robustness, scalability, and operational efficiency of blockchain applications. This will contribute to the development of secure and intelligent decentralized systems, such as Web3. Web3 represents the next evolution of the internet, emphasizing decentralization, user control, and enhanced privacy. By incorporating advanced AI models, I strive to push the boundaries of what is possible in this new paradigm to foster innovation and trust in decentralized technologies.

💼RA@SUSTech (2021 to 2023)

In SUSTech, I had the opportunity to work on various research projects focusing on the optimization and security of blockchain networks. More specifically, I worked on algorithm design and system optimization using game theory and machine learning to improve the throughput of blockchain networks and significantly reduce transaction latency.

• We propose a user-centric blockchain model that enhances data sharing security and improves processing efficiency for Industry 5.0,. This model provides robust support for addressing scalability and low-latency issues in industrial blockchain applications. Through these optimization algorithms, we enhance the overall performance, operability, and reliability of blockchain networks in practical applications.

• We develop a Lagrange Coded Federated Learning (L-CoFL) model to enhance data security and model accuracy in decentralized environments, such as blockchain networks. This model combines federated learning with coded distributed computing to ensure robust data privacy and efficient model updates within the network. Experimental results show that the L-CoFL model significantly shortens the model training cycle while maintaining or even improving prediction accuracy, while demonstrating great potential and advantages in real-time blockchain applications.

• We design a comprehensive blockchain-based solution for Internet of Vehicles (IoV) applications. The framework includes high-throughput blockchain architectures and secure data-sharing models specifically for IoV. By thoroughly analyzing the needs and challenges of IoV, we design blockchain architecture optimized for high-throughput and low-latency environments. The solution covers high-throughput, low-latency blockchain architectures and integrate secure and reliable data-sharing models. This effectively addresses the scalability and security issues faced by blockchain technology in industrial and vehicle applications. We provide strong technical support and assurance for the upgrade and transformation of modern industrial systems and intelligent transportation systems through a series of technological innovations and practical verifications.

🎓PhD Studies@BIT (2016-2023)

During my PhD tenure in BIT, I worked on several innovative algorithms and solutions. Specially we achieve significant progress in optimizing Software-Defined Information-Centric Networking (SD-ICN). Our research results have made important contributions to the fields of the Internet of Things (IoT), Vehicular Ad-hoc Networks (VANETs), and Flying Ad-hoc Networks (FANETs), focusing on enhancing Quality of Service (QoS), optimizing resource utilization efficiency, and improving network reliability and security.

• We innovate a novel SD-ICN solution for FANETs, which performs data dissemination and delivery through an analytical optimization model, emphasizing traffic classification and coordination between UAVs, sensors, and control units. The solution, incorporating advanced optimization algorithms, effectively addresses data transmission issues in high-mobility environments. Experimental analysis shows that this solution outperforms baseline SDN-based UAV implementations in terms of throughput, computational load, handover latency, packet loss, and end-to-end delay. This research not only enhances the overall performance of FANETs but also provides valuable reference points for future optimization of UAV networks.

• We develop an innovative pure SD-ICN framework for IoT, optimizing real-time data delivery, effective caching, reducing overhead, and communication costs, significantly improving the overall performance of IoT networks. The proposed framework enhances the efficiency and reliability of data transmission among IoT devices by introducing new mechanisms for data discovery and dissemination. These improvements not only increase the overall performance of the network but also reduce overhead and costs during data transmission. We demonstrate significant performance improvements in throughput, flow processing rate, caching efficiency, packet loss, and communication overhead through ndnSIM (a variant of NS-3) evaluations.

• We design a dynamic and reliable SD-ICN solution for IoV/VANETs. The optimized SDN data supports the ICN message structure, communication, and slicing model. This allows vehicles and other nodes to perform service-oriented tasks through OpenFlow-enabled SDN controllers. The solution leverages the hierarchical distributed architecture of 5G, deploying multiple controllers at the roadside, edge, and cloud to support V2V and V2I communication. Experimental results show that the model improves data delivery ratio, dissemination, retrieval, and caching efficiency, demonstrating its scalability and reliability. This research provides important technical support for the future optimization of intelligent transportation systems.