About Me
I am currently a second-year Master’s student in Computer Engineering at the University of Illinois Urbana-Champaign, working with Prof. Varun Chandrasekaran. My research interests include Trustworthy ML, AI Safety and Applied Cryptography.
I feel fortunate to have met many kind and talented professors who have supported and inspired my journey. I am grateful to Prof. David Heath and Prof. Yupeng Zhang, whose guidance introduced me to the fascinating world of cryptography, and to Prof. Varun Chandrasekaran, whose mentorship has been invaluable in beginning my academic journey.
Before joining UIUC in 2023, I earned my Bachelor’s degree in Computer Engineering through a joint program between the University of Illinois Urbana-Champaign and Zhejiang University. During my undergraduate studies, I had the privilege of working with Prof. Wee-Liat Ong, Prof. Gaoang Wang, and Prof. Thomas Honold.
I’m open to collaborating on interesting research ideas. If you are interested in Trustworthy ML/AI Safety and seek collaboration, please feel free to contact me at taoranl2 [at] illinois [dot] edu.
“Stay hungry, stay foolish.”
— Steve Jobs, Stanford Commencement Address, 2005
Education
University of Illinois, Urbana-Champaign, 2023 - Present
Master of Engineering in Computer Engineering
Zhejiang University, 2018 - 2023
Bachelor of Engineering in Computer Engineering
University of Illinois, Urbana-Champaign, 2018 - 2023
Bachelor of Science in Computer Engineering
“纸上得来终觉浅，绝知此事要躬行。”
— 陆游《冬夜读书示子聿》
Research Interests
My research broadly covers computer security and privacy, focusing on robust, scalable, and practical solutions in the following domains:
- Trustworthy Machine Learning
- Secure and Practical AI Systems: Creating robust, resilient AI models that withstand adversarial attacks and maintain user data privacy.
- Machine Unlearning: Investigating efficient algorithms and frameworks for safely removing data from trained machine learning models, ensuring regulatory compliance and privacy protection.
- AI for Code Generation, Debugging, Translation, and Vulnerability Detection: Leveraging large language models to automate secure coding practices, improve debugging effectiveness, facilitate code translation, and identify potential security vulnerabilities.
- Integrating Cryptographic Protocols with ML Models: Utilizing cryptographic solutions like MPC and zero-knowledge proofs to develop privacy-preserving machine learning frameworks.
- Security and Privacy of IoT and Cyber-Physical Systems
- Secure AI Integration in Robotics and Autonomous Vehicles: Developing secure, resilient AI solutions to ensure reliable operation and safety in robotic systems and autonomous transportation.
- Enhancing Safety and Resilience of Interconnected Systems: Strengthening security frameworks for IoT-enabled smart infrastructures, enhancing their capability to resist cyber-attacks and privacy breaches.
- Cryptography
- Secure Multi-party Computation (MPC): Developing efficient MPC protocols to enable privacy-preserving analytics for sensitive data, such as healthcare records or financial transactions.
- Zero-knowledge Proofs (zk-SNARK): Designing and optimizing efficient, scalable zk-SNARK solutions for privacy-preserving applications in blockchain and decentralized systems.
- Data Privacy: Building scalable cryptographic mechanisms that protect privacy without compromising data utility or performance.