研究方向：

• 智能成像与图像分析：研究高性能、可解释、自监督的智能图像分析方法，探索成像仪器与人工智能融合新架构，以智能计算突破观测极限，推动科学发现。

• 科学观测大模型：构建跨尺度、多模态、高质量的科学观测数据集，发展面向前沿科学研究的基础模型，实现复杂科学现象的精准表征与机理发现。

• 先进成像机制与系统：研究成像机器人新架构，以具身智能赋能自主感知与主动观测；研究量子效应与光学成像融合新范式，从物理机制上突破观测极限。

• 提出一系列智能方法推进观测极限，为科学研究提供了新方法和新工具。相关工作已在耶鲁大学、马克斯·普朗克研究所、波士顿大学等知名研究机构得到应用，有力推动人工智能与科学观测的交叉融合，为神经科学、免疫学、医学等领域的重要科学发现提供了关键支撑。

• 研究成果以第一/通讯作者发表于Nature Methods、Nature Biotechnology、Nature Computational Science等高影响力国际期刊，受邀在Nature Methods发表技术评论和领域展望，科研成果被《人民日报（海外版）》、《光明日报》等主流媒体报道。

• 担任WAIC 2025, ACAIC 2025, CICAI 2023等学术会议特邀报告嘉宾，凭借在“人工智能赋能科学观测，推动科学发现”方面的突出贡献，荣获PhotoniX Prize技术突破奖。

[1] Yixin Li, Qi Zhang, Yuanlong Zhang, Jiaqi Fan, Zhi Lu, Xinhong Xu, Xinyang Li#, et al. Unsupervised transfer learning enables multi-animal tracking without training annotation, Nature Methods, 2026.

[2] Xinyang Li*, Yixin Li*, Yiliang Zhou, Jiamin Wu, et al. "Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit." Nature Biotechnology, 2023.

[3] Xinyang Li*, Yuanlong Zhang*, Jiamin Wu#, Qionghai Dai#. "Challenges and opportunities in bioimage analysis." Nature Methods, 2023.

[4] Xinyang Li*, Guoxun Zhang*, Jiamin Wu, Yuanlong Zhang, et al. "Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising." Nature Methods, 2021.

[5] Xinyang Li*, Xiaowan Hu*, Xingye Chen*, Jiaqi Fan, et al. "Spatial redundancy transformer for self-supervised fluorescence image denoising." Nature Computational Science, 2023.

[6] Xinyang Li*, Guoxun Zhang*, Hui Qiao*, Feng Bao, et al. (2021). Unsupervised content-preserving transformation for optical microscopy. Light: Science & Applications, 2021.

[7] Zhi Lu*, Wentao Chen*, Feihao Sun, Jiaqi Fan, Xinyang Li, et al. Leveraging spatial-angular redundancy for self-supervised denoising of 3D fluorescence imaging without temporal dependency. Nature Communications, 2025.

[8] Zhifeng Zhao*, Yiliang Zhou*, Bo Liu*, Jing He*, Jiayin Zhao, Yeyi Cai, Jingtao Fan, Xinyang Li, et al. "Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue." Cell, 2023.

[9] Guoxun Zhang*, Xiaopeng Li*, Yuanlong Zhang*, Xiaofei Han, Xinyang Li, et al. Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy. Nature Methods, 2023.

[10] Yuanlong Zhang*, Guoxun Zhang*, Xiaofei Han, Jiamin Wu, Ziwei Li, Xinyang Li, et al. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nature Methods, 2023.