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Dr. Luo is broadly interested in computational biology and machine learning, with a focus on developing AI methods to reveal core scientific insights into biology and medicine.
Dr. Luo teaches fundamental topics and applications at the intersection of computational biology and AI. His teaching philosophy centers on engaging, challenging, and inspiring students.
1. Li, Ziang, and Yunan Luo. "Generalizable and scalable protein stability prediction with rewired protein generative models." Nature Communications (2025). DOI: https://doi.org/10.1038/s41467-025-67609-4
2. Luo, Jiaqi, and Yunan Luo. "Learning maximally spanning representations improves protein function annotation." In International Conference on Research in Computational Molecular Biology (RECOMB), pp. 420-423. Cham: Springer Nature Switzerland, 2025.
3. Ding, Kerr, Michael Chin, Yunlong Zhao, Wei Huang, Binh Khanh Mai, Huanan Wang, Peng Liu, Yang Yang, and Yunan Luo. "Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering." Nature Communications 15, no. 1 (2024): 6392. DOI: https://doi.org/10.1038/s41467-024-50698-y
4. Luo, Yunan, Yang Liu, and Jian Peng. "Calibrated geometric deep learning improves kinase–drug binding predictions." Nature Machine Intelligence 5.12 (2023): 1390-1401. DOI: https://doi.org/10.1038/s42256-023-00751-0
5. Yu, Tianhao, Haiyang Cui, Jianan Canal Li, Yunan Luo, Guangde Jiang, and Huimin Zhao. "Enzyme function prediction using contrastive learning." Science 379, no. 6639 (2023): 1358-1363. DOI: https://doi.org/10.1126/science.adf2465