Guoli Song | Robotics | Research Excellence Award

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Prof. Guoli Song | Robotics | Research Excellence Award

Prof. Guoli Song | Robotics | Researcher at Shenyang Institute of Automation Chinese Academy of Sciences | China

Prof. Guoli Song is an accomplished researcher known for his extensive contributions to medical image analysis, biomedical signal processing, robotics-assisted diagnostics, and intelligent healthcare systems, emerging as a leading figure in the integration of artificial intelligence with modern medical technologies. Prof. Guoli Song completed his higher education at the Shenyang Institution of Automation, Chinese Academy of Sciences, where he earned his doctoral degree with a research focus on computational imaging, intelligent robotics, and medical data interpretation, building a strong academic foundation that continues to support his multidisciplinary scholarship. Over the course of his professional career, he has served in prominent research roles within the Chinese Academy of Sciences, where he has participated in several high-impact international projects involving automated disease detection, AI-based brain tumor segmentation, noninvasive biosensing technologies, and robotic navigation systems for clinical applications. His research interests span medical image registration, deep learning–based diagnosis, biomedical signal processing, optimization frameworks, force-sensing technologies, and computational neuroscience, demonstrating a broad intellectual range supported by strong analytical and technical skills. Prof. Guoli Song is highly proficient in designing advanced machine-learning algorithms, developing intelligent diagnostic pipelines, implementing robotics control architectures, and conducting large-scale computational experiments, which have led to publications in IEEE platforms, Scopus-indexed journals, and other reputable venues with a citation impact exceeding several hundreds. His work has earned recognition through academic honors, research excellence acknowledgments, and invitations to contribute to international collaborations, conferences, and journal review boards. He is actively engaged in professional communities and holds affiliations with respected organizations such as IEEE and ACM, reflecting his commitment to maintaining global research standards and fostering scientific knowledge exchange. Notably, his awards and honors stem from his contributions to intelligent medical systems, advanced diagnostic models, and cross-disciplinary engineering innovations. With a strong record of publications, including influential works on medical image segmentation, biosensing devices, gaze estimation for surgical robots, and hybrid feature-based diagnostic frameworks, Prof. Guoli Song continues to advance cutting-edge methodologies that shape the future of automated healthcare. His continued efforts toward developing efficient, accurate, and clinically relevant technologies highlight his ongoing potential for leadership and innovation. Prof. Guoli Song’s accomplishments, research influence, and future-oriented vision firmly establish him as a leading contributor to global scientific advancement and position him for sustained excellence in medical engineering and computational health research.

Academic Profile: Scopus | Google Scholar

Featured Publications:

  1. Song, G., Han, J., Zhao, Y., Wang, Z., & Du, H. (2017). A review on medical image registration as an optimization problem. 119 citations.

  2. Hao, Z., Luo, Y., Huang, C., Wang, Z., Song, G., Pan, Y., et al. (2021). An intelligent graphene-based biosensing device for cytokine storm syndrome biomarkers detection in human biofluids. 92 citations.

  3. Huang, Z., Zhao, Y., Liu, Y., & Song, G. (2021). GCAUNet: A group cross-channel attention residual UNet for slice-based brain tumor segmentation. 88 citations.

  4. Song, G., Huang, Z., Zhao, Y., Zhao, X., Liu, Y., Bao, M., et al. (2019). A noninvasive system for the automatic detection of gliomas based on hybrid features and PSO-KSVM. 54 citations.

  5. Deng, Y., Yang, T., Dai, S., & Song, G. (2020). A miniature triaxial fiber optic force sensor for flexible ureteroscopy. 50 citations.