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Shuhao Fu

Ph.D. Candidate
UCLA
fushuhao (at) g.ucla.edu


About Me

I’m currently in my fourth year of studying for my Ph.D. in Psychology at the University of California, Los Angeles. I’m fortunate to have Professor Hongjing Lu as my advisor. Before starting my Ph.D., I earned my B.S. in Computer Science and Mathematics from the Hong Kong University of Science and Technology under the guidance of Professor Qifeng Chen. I also had the pleasure of spending a fantastic year in the CCVL lab at Johns Hopkins University, working under the mentorship of Professor Alan L. Yuille. Additionally, I gained valuable experience during my internships at Google X and Mineral.ai.

Research Interests

My research focuses on merging machine learning and cognitive science to enhance relational reasoning and visual analogy in AI models. I investigate how humans perceive and reason about relationships and analogies, with the goal of imbuing AI systems with similar cognitive abilities. Through empirical studies and computational modeling, I bridge the gap between human intelligence and artificial intelligence. I hope to contribute to creating more interpretable, robust, and adaptable AI systems while advancing our understanding of human cognition.

Publications

  1. Nat Comm
    Zero-shot visual reasoning through probabilistic analogical mapping
    Taylor Webb*, Shuhao Fu*, Trevor Bihl, Keith J Holyoak, Hongjing Lu (*Equal contribution)
    Nature Communication 14, 5144 (2023).
    PDF

  2. CogSci
    Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping
    Nicholas Ichien, Qing Liu, Shuhao Fu, Keith J. Holyoak, Alan L. Yuille, Hongjing Lu
    Cognitive Science, 47: e13347 (2023).
    PDF

  3. CogSci
    Human similarity judgments of emojis support alignment of conceptual systems across modalities
    Bryor Snefjella, Yiling Yun, Shuhao Fu, Hongjing Lu
    Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45), 2023.
    PDF

  4. MICCAI
    Domain adaptive relational reasoning for 3d multi-organ segmentation
    Shuhao Fu, Yongyi Lu, Yan Wang, Yuyin Zhou, Wei Shen, Elliot Fishman, Alan Yuille
    International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.
    PDF Early Accept

  5. RSEML
    Attack-Resistant Federated Learning with Residual-based Reweighting
    Shuhao Fu, Chulin Xie, Bo Li, Qifeng Chen
    AAAI 2021 Workshop: Towards Robust, Secure and Efficient Machine Learning (RSEML), 2021.
    PDF Code Bachelor's thesis

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