陳 民瑞（Minrui Chen）
陳 民瑞（Minrui Chen）
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PhD Student
Department of Information Science and Technology
The Graduate School of Information Science and Electrical Engineering
Kyushu University
Minrui Chen is now a PhD student in the Saigo Lab, the Department of Information Science and Technology, Graduate School of Information Science and Electrical Engineering(ISEE), Kyushu University, under the supervision of Prof. Hiroto Saigo. Currently, he is also a visiting student in Prof. Fajie Yuan’s lab at Westlake University. He obtained his BSc Hons degree in Computer Science from Macau University of Science and Technology under the supervision of Prof. Qinglin Zhao in 2023, and his MPhil degree from Kyushu University in 2026.
Minrui Chen is now a PhD student in the Saigo Lab, the Department of Information Science and Technology, Graduate School of Information Science and Electrical Engineering(ISEE), Kyushu University, under the supervision of Prof. Hiroto Saigo. Currently, he is also a visiting student in Prof. Fajie Yuan’s lab at Westlake University. He obtained his BSc Hons degree in Computer Science from Macau University of Science and Technology under the supervision of Prof. Qinglin Zhao in 2023, and his MPhil degree from Kyushu University in 2026.
My research interests lie in computational biology and machine learning, with a particular focus on single-cell and multimodal data analysis. I hope to pursue timely and original research that is well aligned with the current era of AI-driven scientific discovery. Specifically, I work on biologically informed representation learning, workflow optimization, and automated analysis frameworks for data integration, trajectory inference, and cell-cell communication. More recently, I have been exploring LLM-guided evolutionary optimization to improve the robustness and biological validity of single-cell and multimodal analysis workflows.
My research interests lie in computational biology and machine learning, with a particular focus on single-cell and multimodal data analysis. I hope to pursue timely and original research that is well aligned with the current era of AI-driven scientific discovery. Specifically, I work on biologically informed representation learning, workflow optimization, and automated analysis frameworks for data integration, trajectory inference, and cell-cell communication. More recently, I have been exploring LLM-guided evolutionary optimization to improve the robustness and biological validity of single-cell and multimodal analysis workflows.
In parallel, I am also interested in integrating formal methods with machine reasoning to ensure soundness and verifiability in large language model–assisted theorem proving.
In parallel, I am also interested in integrating formal methods with machine reasoning to ensure soundness and verifiability in large language model–assisted theorem proving.
Publications
Publications
#2026
5. Minrui Chen, Huidong Jiang, Hiroto Saigo. SAPSE-Synergy: Balancing Formal Soundness and Empirical Coverage in Neural Theorem Proving. The 27th International Symposium on Formal Methods (FM 2026), Tokyo, Japan, 2026.
# 2025
4. Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou, Minqi Chen, Rong Tian, Hiroto Saigo. An Integrated Deep Learning Approach for the Differential Diagnosis of FUO using Clinical and PET/CT Imaging Data. The 13th Integrated Conference on Bioinformatics for Life and Medical Sciences (IIBMP 2025), Nagoya, Japan, 2025.
3. Yi Zhou, Li-Xiang Yang, Minrui Chen, Ming-Gang Su, Rong Tian. Development and validation of 18F-FDG PET/CT-based multivariable clinical prediction models for etiological types of fever of unknown origin. Quantitative Imaging in Medicine and Surgery. 2025;15(10):9238.
2. Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou, Minqi Chen, Rong Tian, Hiroto Saigo. MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin. arXiv preprint arXiv:2502.04794, 2025. Available at: https://arxiv.org/abs/2502.04794.
# 2024
Minrui Chen, Hiroto Saigo. Scalable Noise-Enhanced Kernel Methods: A Comparative Study of Tanimoto and Gaussian Kernels for Large-Scale Data. The 27th Information-based Induction Sciences Workshop (IBIS'27), Omiya, Saitama, Japan, 2024.
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Selected Awards
2019-2023, Excellent Scholarship, Macau University of Science and Technology
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