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I am mainly engaged in the field of intelligent photonics, aiming at breaking through the spatiotemporal bandwidth limit of traditional optical microscopy and the bottlenecks of computing power and energy efficiency of existing electronic computing through the cross-innovation of optical engineering and artificial intelligence. As the first/corresponding author, I have published more than ten research papers in journals such as Nature Methods and Nature Biotechnology.
Department of Automation, Tsinghua University , Control Science and Engineering , Bachelor's Degree , Undergraduate (Bachelor’s degree)
Tsinghua University , Management Science , Bachelor's Degree in Management , Double Degree Programs (Undergraduate)
Department of Automation, Tsinghua University , Control Science and Engineering , Doctoral degree , Postgraduate (Doctoral)
Department of Precision Instrument, Tsinghua University Assistant Professor
Department of Automation, Tsinghua University Postdoc
C. Qiao, S. Liu, Y. Wang, W. Xu, X. Geng, T. Jiang, J. Zhang, Q. Meng, H. Qiao*, D. Li*, and Q. Dai*. A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification, Nature Biotechnology, 1-10, 2025.
C. Qiao, Z. Li, Z. Wang, Y. Lin, C. Liu, S. Zhang, Y. Liu, Y. Feng, X. Yang, W. Fu, X. Dong, J. Guo, W. Xu, X. Wang, T. Jiang, Q. Meng, Q. Wang, Q. Dai*, D. Li*. Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. Nature Methods, 1-11, 2025.
C. Qiao, D. Li, Y. Liu, S. Zhang, K. Liu, C. Liu, Y. Guo, T. Jiang, C. Fang, N. Li, Y. Zeng, K. He, X. Zhu, J. Lippincott-Schwartz*, Q. Dai*, D. Li*. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nature Biotechnology, 41(3): 367-377, 2023. (Cover article, ESI highly cited paper)
C. Qiao, D. Li, Y. Guo, C. Liu, T. Jiang, Q. Dai*, D. Li*. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nature Methods, 18(2): 194-202, 2021. (ESI highly cited paper)
C. Qiao, Y. Zeng, Q. Meng, X. Chen, H. Chen, T. Jiang, R. Wei, J. Guo, W. Fu, H. Lu, D. Li, Y. Wang, H. Qiao, J. Wu, D. Li*, and Q. Dai*. Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy. Nature Communications, 15: 4180, 2024. (Top 25 Physics paper of the Jounal in 2024)
C. Qiao, H. Chen, R. Wang, T. Jiang, Y. Wang*, and D. Li*. Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging. Photonics Research, 12: 474, 2024. (Cover article)
C. Qiao, X. Chen, S. Zhang, D. Li, Y. Guo, Q. Dai*, D. Li*. 3D Structured Illumination Microscopy via Channel Attention Generative Adversarial Network. IEEE Journal of Selected Topics in Quantum Electronics, 27(4): 1-11, 2021.
C. Qiao, H. Qiao, J. Wu, X. Li, J. Fan, Q. Dai. Deep learning based tomographic phase microscopy with blind structured illumination. Biophotonics Congress: Optics in the Life Sciences Congress 2019. (Oral)
X. Chen, C. Qiao*, T. Jiang, J. Liu, Q. Meng, Y. Zeng, H. Chen, H. Qiao, D. Li*, and J. Wu*. Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging. PhotoniX, 5: 4, 2024.
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