I am a web security researcher at Palo Alto Networks in the San Francisco Bay Area, where I develop machine learning models to detect and prevent web attacks, including advanced phishing pages and runtime assembly attacks. Previously, I was a researcher at Huawei Research Canada, where I focused on supply chain security and vulnerability detection using agentic approaches.
I hold a Ph.D. in Computer Engineering from the University of Toronto, advised by Prof David Lie and Prof Lianying Zhao. My doctoral research explored machine learning approaches to improve the security and privacy of web requests. I completed my Master of Applied Science under Prof David Lie and my Bachelor at the University of Toronto.
Professional Activities
Program Committees
2027
- USENIX Security Symposium (USENIX Security)
2026
- Trustworthy AI for Good Workshop at the International Conference on Machine Learning (AI4Good @ ICML) [Organizing & Program Committee]
- Trustworthy AI for Good Workshop at the Conference on Neural Information Processing Systems (AI4Good @ NeurIPS) [Organizing & Program Committee]
- International Conference on Privacy, Security and Trust (PST)
- Symposium on Electronic Crime Research (eCrime)
- IEEE Transactions on Dependable and Secure Computing (TDSC)
2025
- IEEE Transactions on Dependable and Secure Computing (TDSC)
News
Agent Tripwire: Detecting Misbehaving AI Agents at Runtime
Agent Tripwire replaces attack classification with runtime invariants: inert tools, credentials, resources and cross-agent markers that no valid execution should touch. A trip triggers containment before the agent can produce a real side effect.
Device Code Phishing Evasion Techniques
Device-code phishing campaigns are combining CAPTCHA gates, multi-step SaaS lure chains, encrypted blob-page delivery and source-level text obfuscation to evade automated scanners and reputation checks.