About me

I obtained my PhD from the University of Toronto, supervised by Amir-massoud Farahmand and Rich Zemel.

Research Goal: My research combines both theoretical and empirical approaches to robustness and generalization in deep learning, with the goal of enabling more reliable AI systems. I currently focus on the safety and security of LLMs, advancing research that drives practical solutions for responsible AI deployment.

Refer to Research and Publications for more information on my research, and to CV for more information about my academic background.

Recent News

May 2025: One paper on LLM jailbreaking accepted at ICML 2025 (spotlight)

Jul 2024: One paper on improving adversarial transferability accepted at ECCV 2024

Apr 2024: One survey paper on adversarial transferability accepted at TMLR

Apr 2024: Selected as a DAAD AInet fellow for the Postdoc-NeT-AI program on Safety and Security in AI

Nov 2023: One journal paper on understanding model robustness accepted at TMLR with Featured Certification (ICLR 2024 journal-to-conference track)

Sep 2022: One paper on data augmentation accepted at BMVC 2022