Mr. Guoxun Zhang | Microscopy | Best Researcher Award

Postdoctoral at University of California, San Francisco, United States

Dr. Guoxun Zhang is a postdoctoral researcher in the Department of Pharmaceutical Chemistry at UCSF, where he works under the mentorship of Prof. Bo Huang. Originally from Beijing, he earned his Ph.D. in Automation from Tsinghua University (2019–2024), where he developed AI-driven microscopy tools. Zhang’s innovative work includes DeepCAD and DeepSeMi—self-supervised frameworks that significantly improve image quality without requiring high-quality training data. His first-author research has been published in top-tier journals like Nature Methods and Light: Science & Applications. With over 700 citations (Google Scholar), he has also contributed to generative AI models for lung CT imaging and brain disease diagnostics. A recipient of multiple excellence scholarships from Tsinghua, Zhang is passionate about integrating large-scale AI models into microscopy to accelerate scientific discovery and drug development.

Professional Profile

ORCID

Google Scholar

Education

Zhang completed a dual bachelor’s program at Tsinghua University, earning degrees in Materials Science and Engineering (2014–2016) and Engineering Physics (2016–2019), mentored by Prof. Kui Ying. He then pursued a Ph.D. in Automation (2019–2024) under Prof. Qionghai Dai, focusing on self-supervised deep learning in microscopy. His doctoral research yielded several transformative methodologies, including DeepCAD and DeepSeMi, published in high-impact journals like Light: Science & Applications and Nature Methods. Throughout his studies, he was awarded prestigious scholarships—such as the Tsinghua Comprehensive Excellence Scholarship (2021–2023) and National Scholarship (2017)—recognizing his academic merit and research innovation. Currently at UCSF, Zhang continues to build on a strong foundation in physics, engineering, and AI-driven microscopy.

Work Experience

In 2025, Zhang joined UCSF as a postdoctoral researcher in Pharmaceutical Chemistry under Prof. Bo Huang, focusing on AI-powered medical imaging. His postdoctoral work aims to integrate generative and vision–text AI models into diagnostics. Previously, at Tsinghua University, Zhang advanced from dual bachelor’s scholar to leading Ph.D. candidate (2019–2024). He co-developed groundbreaking self-supervised neural denoising frameworks for microscopy—DeepCAD and DeepSeMi—and authored first-author papers in Nature Methods. He contributed significantly to projects involving respiratory CT foundation models and multimodal AI copilot tools. Zhang has been invited to speak at institutions such as Harvard Medical School and Xi’an Jiaotong University. His growing professional trajectory combines deep technical expertise with communication and leadership in interdisciplinary science.

🏆 Awards & Honors

Dr. Zhang’s academic excellence has been recognized through multiple awards. He received the National Scholarship (2017) and Volunteer Excellence Scholarship (2017), along with the Engineering Physics Academic Excellence Scholarship at Tsinghua (2017). He was awarded the Science and Technology Excellence Scholarship in 2018, followed by the Comprehensive Excellence Scholarship for three consecutive years (2021–2023). His research portfolio—highlighted by multiple publications in premier journals—has been cited over 700 times. Zhang’s accomplishments include first-author papers in Nature Methods and Light: Science & Applications. As a distinguished speaker, he has delivered invited talks at Harvard Medical School, Xi’an Jiaotong University, and Westlake University in 2024. His accolades reflect both scholarly prowess and his contributions to the evolving field of AI-enabled biomedical imaging.

Research Focus

Zhang’s research centers on harnessing AI—particularly self-supervised deep learning and large vision models—to revolutionize biomedical imaging. His innovations in neural denoising for microscopy, such as DeepCAD and DeepSeMi, enable high-quality imaging with minimal annotated data, transforming capabilities in cell biology and neuroscience. He also develops generative foundation models for lung CT scans to aid disease detection, alongside multimodal vision–text AI copilot systems tailored for brain diagnostics. Additionally, Zhang explores adaptive optics and in silico simulation to simulate and interpret complex biological imaging. His strategy is rooted in low-data deep learning methods—such as unsupervised image restoration and simulation-driven training—that are cost-effective and scalable. By merging cutting-edge AI with life science applications, Zhang aims to accelerate early disease screening, drug discovery, and clinical translation.

Skills

Dr. Zhang excels in constructing self-supervised deep learning frameworks for image denoising, leveraging strong expertise in Python, TensorFlow/PyTorch, and neural network architectures. He is proficient in building and validating generative foundation models for medical imaging. His skill set spans simulation-guided deep learning, adaptive optics principles, and quantitative analysis of microscopy data. Zhang is adept at academic scientific writing, data visualization, and statistical interpretation. He collaborates across disciplines, as evidenced by co-authoring projects in respiratory CT AI and neuroscience. His strong communication abilities are evidenced by invited talks at prestigious institutions. He uses version control and deployment with Git, Docker, and HPC environments. Zhang is experienced in mentoring junior researchers, managing project timelines, and developing open-source AI toolkits, emphasizing reproducibility and user engagement in computational imaging.

Conclusion

Dr. Guoxun Zhang is a strong and deserving candidate for the Best Researcher Award. His innovative, interdisciplinary research at the intersection of artificial intelligence and biomedical imaging has already influenced multiple scientific domains. With continued focus on translational outcomes and leadership expansion, Dr. Zhang is well-positioned to become a global leader in computational biomedicine. The award would recognize not only his current excellence but also encourage continued high-impact contributions.

Publications to Noted

Title: Iterative tomography with digital adaptive optics permits hour-long intravital observation of 3D subcellular dynamics at millisecond scale
Authors: Jiamin Wu, Zhi Lu, Dong Jiang, Yuduo Guo, Hui Qiao, Yi Zhang, Tianyi Zhu, Yeyi Cai, Xu Zhang, Karl Zhanghao, Hao Xie, Tao Yan, Guoxun Zhang, Xiaoxu Li, Zheng Jiang, Xing Lin, Lu Fang, Bing Zhou, Peng Xi, Jingtao Fan, Li Yu, Qionghai Dai
Citations: 206
Year: 2021
Journal: Cell

Title: Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising
Authors: Xinyang Li, Guoxun Zhang, Jiamin Wu, Yuanlong Zhang, Zhifeng Zhao, Xing Lin, Hui Qiao, Hao Xie, Haoqian Wang, Lu Fang, Qionghai Dai
Citations: 156
Year: 2021
Journal: Nature Methods

Title: Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit
Authors: Xinyang Li, Yixin Li, Yiliang Zhou, Jiamin Wu, Zhifeng Zhao, Jiaqi Fan, Fei Deng, Zhaofa Wu, Guihua Xiao, Jing He, Yuanlong Zhang, Guoxun Zhang, Hui Qiao, Hao Xie, Yulong Li, Haoqian Wang, Lu Fang, Qionghai Dai
Citations: 135
Year: 2023
Journal: Nature Biotechnology

Title: Unsupervised content-preserving transformation for optical microscopy
Authors: Xinyang Li, Guoxun Zhang, Hui Qiao, Feng Bao, Yue Deng, Jiamin Wu, Yangfan He, Jingping Yun, Xing Lin, Hao Xie, Haoqian Wang, Qionghai Dai
Citations: 126
Year: 2021
Journal: Light: Science & Applications

Title: Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data
Authors: Yuanlong Zhang, Guoxun Zhang, Xiaofei Han, Jiamin Wu, Ziwei Li, Xinyang Li, Guihua Xiao, Hao Xie, Lu Fang, Qionghai Dai
Citations: 42
Year: 2023
Journal: Nature Methods

Title: Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy
Authors: Guoxun Zhang, Xiaopeng Li, Yuanlong Zhang, Xiaofei Han, Xinyang Li, Jinqiang Yu, Boqi Liu, Jiamin Wu, Li Yu, Qionghai Dai
Citations: 29
Year: 2023
Journal: Nature Methods

Title: Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution
Authors: Hao Xie, Xiaofei Han, Guihua Xiao, Hanyun Xu, Yuanlong Zhang, Guoxun Zhang, Qingwei Li, Jing He, Dan Zhu, Xinguang Yu, Qionghai Dai
Citations: 15
Year: 2024
Journal: Nature Biomedical Engineering

Title: 3D observation of large-scale subcellular dynamics in vivo at the millisecond scale
Authors: Jiamin Wu, Zhi Lu, Hui Qiao, Xu Zhang, Karl Zhanghao, Hao Xie, Tao Yan, Guoxun Zhang, Xiaopeng Li, Qionghai Dai
Citations: 6
Year: 2019
Journal: bioRxiv

Title: Artificial Intelligence Enhanced Digital Nucleic Acid Amplification Testing for Precision Medicine and Molecular Diagnostics
Authors: Yuanyuan Wei, Xiang Liu, Changran Xu, Guoxun Zhang, Wu Yuan, Ho-Pui Ho, Ming Xu
Citations: 4
Year: 2024
Journal: arXiv preprint

Title: Real-time denoising of fluorescence time-lapse imaging enables high-sensitivity observations of biological dynamics beyond the shot-noise limit
Authors: Xinyang Li, Yixin Li, Yiliang Zhou, Jiamin Wu, Zhifeng Zhao, Jiaqi Fan, Fei Deng, Zhaofa Wu, Guihua Xiao, Jing He, Guoxun Zhang, et al.
Citations: 4
Year: 2022
Journal: bioRxiv

Guoxun Zhang | Microscopy | Best Researcher Award