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Huiqi Zou

MSE in Computer Science
Johns Hopkins University
hzou11(at)jhu(dot)edu


About Me

Hi, I am Huiqi Zou, an incoming PhD student working under the guidance of Prof. Weiyan Shi and Prof. Yanzhi Wang at Northeastern University. I hold a Master’s degree in Computer Science from Johns Hopkins University, where I completed a thesis under the supervision of Prof. Ziang Xiao.
Previously, I earned a Bachelor’s degree in Computer Science (Artificial Intelligence Stream) from City University of Hong Kong, where I completed my final year project under the supervision of Prof. Linqi Song.

Research Interests

My research interest lies in natural language processing (NLP) and human-computer interaction (HCI), especially in the field of interactive evaluation. I am currently fascinated by the following research questions:

My ultimate goal is to build interactive systems for more engaging human-computer communication and transition from static evaluation methods to more flexible, task-driven assessments that capture the diverse scenarios in which models are deployed.

News

Publications

  1. IEEE TSE
    Practitioners’ Expectations on Log Anomaly Detection
    X. Ma, Y. Li, J. Keung, X. Yu, H. Zou, Z. Yang, F. Sarro, and E. T. Barr
    IEEE Transactions on Software Engineering, 2025.
    PDF

  2. arxiv
    Personality Structured Interview for Large Language Model Simulation in Personality Research
    P. Wang, H. Zou, H. Chen, T. Sun, Z. Xiao, and F. Oswald
    arxiv, 2025.
    PDF Code

  3. IEEE TSE
    On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations
    X. Ma, H. Zou, P. He, J. Keung, Y. Li, X. Yu, and F. Sarro
    IEEE Transactions on Software Engineering, 2024.
    PDF Code

  4. arxiv
    Can LLM "Self-report"?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots
    H. Zou, P. Wang, Z. Yan, T. Sun, and Z. Xiao
    arxiv, 2024.
    PDF Code

  5. OSF Preprints
    Not Yet: Large Language Models Cannot Replace Human Respondents for Psychometric Research
    P. Wang, H. Zou, Z. Yan, F. Guo, T. Sun, Z. Xiao, and B. Zhang
    OSF Preprints, 2024.
    PDF Code

  6. IEEE Trans. Reliab
    AttSum: A Deep Attention-based Summarization Model for Bug Report Title Generation
    X. Ma, J. Keung, X. Yu, H. Zou, J. Zhang, and Y. Li
    IEEE Transactions on Reliability, 2023.
    PDF Code

  7. ICSR
    Generating Natural Language Responses in Robot-Mediated Referential Communication Tasks to Simulate Theory of Mind
    Z. Liu*, Y. Qin*, H. Zou*, E. J. Paek, D. Casenhiser, W. Zhou, and X. Zhao
    International Conference on Social Robotics, 2022.
    PDF

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