phd student, University of Electronic Science and Technology of China, China
LiangJun Deng is a dynamic Ph.D. student at the University of Electronic Science and Technology of China (UESTC), specializing in Software Engineering. His research explores cutting-edge technologies such as WebAssembly, Large Language Models (LLMs), IoT, symbolic execution, and blockchain. Deng is actively engaged in pushing the boundaries of software security through automation and formal verification platforms. With five published research projects, multiple patents, and growing citation impact, he is a rising researcher known for his innovations in AI-driven software analysis and secure computing environments.
Publication Profile
Google Scholar
🎓 Education Background
LiangJun Deng is currently pursuing his Ph.D. at the School of Information and Software Engineering, UESTC, China. His academic training is grounded in software engineering, and he has cultivated a strong foundation in secure system design and intelligent code analysis using LLMs and symbolic execution techniques.
💼 Professional Experience
In addition to his academic work, Deng has participated in over 10 consultancy and industry-sponsored projects. These real-world collaborations span across sectors like IoT, blockchain, and medical systems, showcasing his applied research capabilities. He has also filed 5 patents and authored 4 journal articles indexed in top databases like SCI and Scopus, contributing to global knowledge in software security.
🏆 Awards and Honors
Deng was honored with the Best Conference Paper Award at EISA 2024 for his pioneering research on LLM-based program instruction analysis. His growing academic influence is marked by 3 citations to date, reflecting the promising impact of his early research contributions.
🔬 Research Focus
LiangJun Deng’s research primarily focuses on multi-language software security leveraging WebAssembly, LLMs, and symbolic execution. He investigates LLM universality across different code representations like source code, ASTs, and binaries. His most recent work introduces LLM-based induction techniques to address path explosion in symbolic execution, significantly enhancing automated software security systems in industrial domains such as healthcare and IoT.
🔚 Conclusion
Through a unique blend of theoretical insight and real-world application, LiangJun Deng is advancing the frontier of intelligent software security. His commitment to high-impact research, combined with technical innovation and collaborative projects, establishes him as a promising contributor to the global tech research community. 🚀
📚 Top Publications
Formal Verification Platform as a Service: WebAssembly Vulnerability Detection Application
🗓️ Year: 2023 | 📘 Journal: Computer Systems Science & Engineering | 🔗 Cited by 3 articles
Focuses on building a platform to detect WebAssembly vulnerabilities for enhanced software security.
GPT-Based Automated Induction: Vulnerability Detection in Medical Software
🗓️ Year: 2025 | 📘 IEEE Journal of Biomedical and Health Informatics | 🔗 Cited by 1 article
Presents a GPT-based method to identify vulnerabilities in critical medical software using symbolic execution.
LLM-Based Program Analysis for Source Codes, Abstract Syntax Trees and WebAssembly Instructions
🗓️ Year: 2024 | 📘 Preprint/Conference Version | 🔗 Citation info pending
Investigates LLM behavior across three key software abstractions for advanced program analysis.
GPT-Based Wasm Instruction Analysis for Program Language Processing
🗓️ Year: 2024 | 📘 International Symposium on Emerging Information Security and Applications (EISA 2024) | 🏅 Best Paper Award
Introduces a novel method of using GPT for processing WebAssembly instructions.
LLM-Based Automated Modeling in Symbolic Execution for Securing Medical Software
🗓️ Year: 2024 | 📘 Preprint/Collaboration Draft | 🔗 Citation info pending
Explores symbolic execution enhanced with LLM-based models for the secure analysis of medical software.