I am Hiring!
I am actively looking for PhD students to join my research group at the University of Sydney. My primary vision is to develop the next generation of AI security agents - systems that can autonomously identify, analyze, and prevent security vulnerabilities, similar to how AI has revolutionized games like Poker. My research interests span across:- AI-powered security agents for vulnerability detection and prevention
- Large security models - using LLMs to enhance existing security tools
- Real-time intrusion detection and prevention 🔔
- Advanced program analysis (fuzzing, symbolic execution)
- Privacy-preserving transaction systems
PhD Candidates 🎓
If you are passionate about pursuing a PhD with me at the University of Sydney, please email me with:- Three bullet points highlighting your achievements (anything)
- At least one technical achievement (research / engineering)
- Your CV
I am Seeking Funding Opportunities!
I am actively seeking funding and collaboration opportunities. My research philosophy emphasizes practical value and real-world impact - I believe the best research not only advances academic understanding but also solves concrete problems in industry. If you have interesting research questions you'd like me to explore, or if you're interested in my research and want to discuss potential opportunities, I'd love to hear from you. I'm always eager to tackle challenging problems that have real-world impact.My Achievements
Rankings do not mean much, but they help illustrate my dedication to high-impact, top-tier research, especially for prospective PhD students.- I am proud to have co-founded D23E.ch, where we are tackling some of the most challenging problems in blockchain security and privacy.
- I am currently ranked 1016th on the Systems Circus worldwide (not normalized by academic age).
- I pioneered the discovery of sandwich attacks in October 2019, reporting them to Uniswap. This has since evolved into a multi-million dollar market for MEV extraction, with platforms like EigenPhi now tracking these activities.
- I have published extensively in top-tier venues including IEEE S&P, USENIX Security, SIGMETRICS, IMC, ICSE, and ISSTA, with several papers making it into the Top-100 Security Papers list.
- I have successfully completed several Ethereum Foundation grants and received bug bounties from both the Ethereum Foundation and Flashbots for responsible vulnerability disclosure.
- My notable papers in security and blockchain:
My Research Summary
Click to expand research summary
Liyi is a researcher specialized in cybersecurity and artificial intelligence. Over the past five years, he
has published extensively in leading international security conferences, and his work has been widely cited.
His research mainly focuses on developing innovative ways to detect software vulnerabilities automatically,
particularly by using advanced machine learning techniques. Liyi has built several practical, effective
systems to identify security threats and anomalies in software. He has hands-on experience includes finding
critical software vulnerabilities, which earned him multiple awards from bug bounty programs. He
collaborates
with top international researchers from universities like UC Berkeley and ETH Zurich, helping to extend the
real-world impact and visibility of his research.
Liyi's current focus is about creating intelligent software tools that can independently identify and
exploit
software vulnerabilities without needing constant human supervision. His work introduces new ways for AI
systems to reason about complex security issues on their own, adapting dynamically to emerging threats and
iteratively refining their strategies. Using real-world data, especially from blockchain security incidents,
Liyi's approach advances current cybersecurity methods through reinforcement learning, automated reasoning,
and focused fine-tuning techniques. This research fills an important gap in existing technologies, moving
cybersecurity systems from simple pattern matching towards more genuine, strategic problem-solving
abilities.
The goal is to significantly improve accuracy and reliability of automated cybersecurity solutions.
Liyi's research is valuable because it creates tools that can automatically identify software
vulnerabilities,
preventing dangerous exploits. These innovations can greatly benefit academic researchers, industries,
governments, and broader society by reducing the need for costly and slow manual security reviews. Liyi's
work
aligns with Australia's national cybersecurity priorities, helping build local expertise in security
technologies. Through mentoring and training the next generation of cybersecurity experts and through strong
international collaborations, Liyi contributes to strengthening national cybersecurity capabilities.
Additionally, insights from his research will shape responsible public policies around artificial
intelligence, protecting important digital infrastructure and enhancing public trust in technology.
Liyi has demonstrated success previously by integrating artificial intelligence effectively with
cybersecurity, shown clearly through practical prototypes and research projects he has completed. He
receives
strong support from the University of Sydney, including access to advanced computing equipment, funding
resources, and expert collaborators in relevant fields. His project strategy focuses on adapting existing
machine learning technologies and using cloud infrastructure to ensure his methods are practical and
scalable.
Initial experiments and prototype systems have already shown promising results, confirming the feasibility
and
potential of his approach.
My CV
Details of my publications, academic services, and experiences. [Download]
