Changtai Li – Short Résumé

Research interests

My work focuses on artificial intelligence and computer vision for various scientific domains, including materials science and biomedical engineering.

Education

09/2021 – Current

PhD in Computer Science (Joint-PhD Program)

University of Groningen, Groningen, the Netherlands

Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence

University of Science and Technology Beijing, Beijing, China

School of Computer and Communication Engineering

09/2017 – 07/2021

Bachelor of Engineering in Computer Science

University of Science and Technology Beijing, Beijing, China

School of Computer and Communication Engineering

Publications

C. Li^†, Y. Zhang^†, Y. Jia, Y. Guo, C. Yao^*, X. Ban, and Y. He^*

HRTEM-GAN: Structure-preserving restoration of low-quality atomic-scale HRTEM images

Nano Research, Volume 19, Issue 10, Article 94908715. SciOpen, 2026.

doi: 10.26599/NR.2026.94908715
C. Li^†, X. Han^†, C. Yao, Y. Guo, Z. Li, L. Jiang, W. Liu, H. Huang, H. Fu^*, and X. Ban^*

A novel training-free approach to efficiently extracting material microstructures via visual large model

Acta Materialia, Volume 290, Article 120962. Elsevier, 2025.

doi: 10.1016/j.actamat.2025.120962
P. Yun, H. Fu^*, H. Zhang, L. Jiang, X. Xiao, S. Zhu, Y. Jia, C. Li, W. Yang, and J. Xie^*

Mechanical and Electrical Properties dataset of Cu-Cr-X alloys generated with automated figure data extraction

Scientific Data, Volume 12, Article 2023. Nature Publishing Group, 2025.

doi: 10.1038/s41597-025-06295-9
C. Li^†, R. Jiang^†, H. Wang, W. Xue, Y. Guo, and X. Ban^*

DeepMMP: Efficient 3D perception of microstructures from serial section microscopic images

Computational Materials Science, Volume 235, Article 112826. Elsevier, 2024.

doi: 10.1016/j.commatsci.2024.112826
R. Jiang^†, C. Li^†, X. Ban^*, S. Yin, C. Yao, Y. Guo, and M. S. Obaidat

From non-expert to expert: Recurrent refined learning for medical image segmentation

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2086–2093. IEEE, 2024.

doi: 10.1109/BIBM62325.2024.10821757
C. Li^†, R. Jiang^†, S. Yin, J. Yang, and X. Ban^*

Self-supervised rotation learning for 3D segmentation on nasopharyngeal carcinoma MRI images

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 3529–3534. IEEE, 2023.

doi: 10.1109/BIBM58861.2023.10385483
B. Zhang, Q. Zhu, C. Xu, C. Li, Y. Ma, Z. Ma, S. Liu, R. Shao, Y. Xu, B. Jiang, L. Gao, X. Pang, Y. He^*, G. Chen^*, and L. Qiao^*

Atomic-scale insights on hydrogen trapping and exclusion at incoherent interfaces of nanoprecipitates in martensitic steels

Nature Communications, Volume 13, Article 3858. Nature Publishing Group, 2022.

doi: 10.1038/s41467-022-31665-x

^† Equal contribution ^* Corresponding author