I'm Yuzhi (Paul) Tang, an MScAC student at the University of Toronto. I am deeply passionate about identifying and mitigating risks in large-scale models. Currently, I'm working at the Machine Learning Group at the University of Toronto with Yangjun Ruan and Honghua Dong, where I developed an automatic pipeline to identify and decompose risks in LLM agent trajectories through prompt-tuning, and extended ToolEmu for large-scale evaluation on state-of-the-art LLM agents. My focus is on enhancing the safety and reliability of AI systems by addressing challenges in large-scale model deployment.
I also received the Undergraduate Student Research Award from NSERC for research in developing a reliability testing framework for computer vision models with Prof. Marsha Chechik. Previously, I applied ML to various healthcare settings, such as adapting a self-supervised pretraining recipe for ultrasound image segmentation at the MiDATA lab with Prof. Pascal Tyrrell and developing an end-to-end ML pipeline for sleep staging at the Sunnybrook Research Institute.
🔭 Current Focus: Risk identification and mitigation in large-scale LLMs.
🌱 Learning: Advanced techniques for improving LLM safety and reliability.
👯 Looking to Collaborate: On projects tackling the complexities and risks of large-scale AI models.
📫 Reach Me: yuzhi.tang@mail.utoronto.ca
⚡ Fun Fact: I won 1st place at the IJCAI 2023 Intrinsic Error Evaluation Competition!