I regularly publish my work at top AI venues (e.g., NeurIPS, CVPR). Here is a list of my selected publications that demonstrate the quality and interdisciplinary contributions of my work. For a full list of my publications, please see my Google Scholar page.
Donnelly, J., Guo, Z., Barnett, A. J., McTavish, H., Chen, C., & Rudin, C. (2025). Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025).
Ma, C., Donnelly, J., Liu, W., Vosoughi, S., Rudin, C., & Chen, C. (2024). Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. In the Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024).
Liu, X., Zhao, H., Tang, Y., Chen, C., Zhu, Y., Song, B., & Li, Y. (2024). Few-shot learning-based generative design of metamaterials with zero Poisson’s ratio. Materials & Design, 244, 113224.
Ma, C., Zhao, B., Chen, C., & Rudin, C (2023). This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. In the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023).
Kimble, M., Allers, S., Campbell, K., Chen, C., Jackson, L. M., King, B. L., Silverbrand, S., York, G., & Beard, K. (2022). medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata. Bioinformatics, 38(19), 4589-4597.
Donnelly, J., Barnett, A. J., & Chen, C. (2022). Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022).
Chen, C., Lin, K., Rudin, C., Shaposhnik, Y., Wang, S., & Wang, T. (2022). A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decision Support Systems, 152, 113647.
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistic Surveys, 16, 1-85.
Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y., & Rudin, C. (2021). A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nature Machine Intelligence, 3(12), 1061-1070.
Chen, C., Li, O., Tao, C., Barnett, A. J., Su, J., & Rudin, C. (2019). This Looks Like That: Deep Learning for Interpretable Image Recognition. In the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019).