Hello, I'm Shenzhe (Cho) Zhu🤔

I am a researcher with interest in Trustworthy AI, LLM Interpretability, Multimodal LLM. Currently, I am a third-year Computer Science student at the University of Toronto, working with Prof. William Cunningham in the SocialAI lab. Additionally, I collaborate remotely with the TsinghuaC3I Lab at Tsinghua University and PRADA Lab at King Abdullah University of Science and Technology(KAUST).

email: cho.zhu at mail.utoronto.ca

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News 📢

Publications/Pre-prints

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, Yang Zhou, Huaxiu Yao, Zhengzhong Tu
arXiv, 2024

AutoTrust, a comprehensive benchmark to evaluate the trustworthiness of DriveVLMs in autonomous driving, uncovering vulnerabilities in safety, robustness, privacy, and fairness using a large visual question-answering dataset and testing six diverse VLMs.

Project Page PDF arXiv Code
What Makes Your Model a Low-empathy or Warmth Person: Exploring the Origins of Personality in LLMs

What Makes Your Model a Low-empathy or Warmth Person: Exploring the Origins of Personality in LLMs

Shu Yang*, Shenzhe Zhu*, Ruoxuan Bao, Liang Liu, Yu Cheng, Lijie Hu, Mengdi Li, Di Wang
NeurIPS 2024 LanGame Workshop

Large language models (LLMs) display human-like traits, but how they form is unclear. We explore how long-term factors and short-term pressures shape these traits and their impact on model safety.

PDF arXiv Code
Exploring knowledge graph-based neural-symbolic system from application perspective

Exploring knowledge graph-based neural-symbolic system from application perspective

Shenzhe Zhu, Shengxiang Sun
arXiv, 2024

This survey paper explores KG-based neural-symbolic integration, enhancing neural reasoning, improving symbolic accuracy, and combining both

PDF arXiv

Projects

Minagen: A Minimal Testing Ground for Building Cognitive Architectures for Generative Agents

Minagen: A Minimal Testing Ground for Building Cognitive Architectures for Generative Agents

Eric Xue, Shenzhe Zhu, Rebekah Gelpi, William A. Cunningham

Minagen develops and tests cognitive architectures for generative AI agents, offering a minimalistic platform for researchers to explore and test cognitive modeling and AI ideas.

Career Navigator: LLM & Knowledge Graph-Based Job Recommender Engine

Career Navigator: LLM & Knowledge Graph-Based Job Recommender Engine

Shenzhe Zhu

Career Navigator is an job recommendation service based on LLM and Knowledge Graph, It addresses challenges students face on the CSM platform, improving job search efficiency.

Code

Experience

  • TsinghuaC3I Lab, Tsinghua University
    Research Intern, advised by Dr. Biqing Qi
    October 2024 - Current
  • Texas A&M University
    Research Intern, advised by Prof. ZhengZhong Tu
    July 2024 - Current
  • PRADA LAB - King Abdullah University of Science and Technology
    Research Intern, advised by Prof. Di Wang
    June 2024 - Current
  • Social AI Lab - University of Toronto
    Research Assistant, advised by Prof. William Cunningham
    May 2024 - Current
  • Urban Data Research Center - University of Toronto
    NLP Analyst
    May 2024 - Aug 2024
  • University of Toronto Scarborough
    Data Analyst Intern
    Jan 2024 - May 2024

Education

  • University of Toronto
    B.S. in Computer Science.
    Fall 2022 - Current