Qingbing Chang | Robotics | Research Excellence Award

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Assoc. Prof. Dr. Qingbing Chang | Robotics | Research Excellence Award

Assoc. Prof. Dr. Qingbing Chang | Robotics | Associate Professor at Northeast Forestry University | China

Assoc. Prof. Dr. Qingbing Chang is an accomplished researcher whose academic journey reflects deep expertise in piezoelectric actuation, micro–nano positioning, advanced robotics, and precision mechatronic engineering, supported by a strong record of scholarly excellence and international collaboration. With a solid educational foundation culminating in a doctoral degree in mechanical and mechatronic systems from a leading research-focused university, Assoc. Prof. Dr. Qingbing Chang has built a distinguished career through progressive academic appointments, interdisciplinary laboratory leadership, and contributions to high-impact collaborative research teams working across robotics, optical systems, and cross-scale actuation technologies. His professional experience spans advanced design of multi-degree-of-freedom piezoelectric devices, robotic micromanipulation platforms, micro–nano motion control systems, actuator modeling, and precision instrument calibration, allowing Him to contribute both theoretical advancements and engineering innovations to the global research community. His research interests include cross-scale robotic actuation, stick–slip mechanisms, inertial actuation, multi-DOF piezoelectric structures, biologically assisted puncture robotics, optical alignment systems, and intelligent robotic mechanisms, with a consistent focus on improving stiffness, decoupling performance, load capacity, velocity, and accuracy across micro-to-macro motion manipulation platforms. Assoc. Prof. Dr. Qingbing Chang’s research skills encompass system modeling, structural optimization, experimental validation, optical and mechanical integration, microfabrication concepts, and high-precision measurement techniques, enabling Him to produce impactful results published in internationally indexed journals. He has contributed to widely cited works in IEEE Transactions on Industrial Electronics, IEEE Transactions on Robotics, IEEE/ASME Transactions on Mechatronics, Mechanical Systems and Signal Processing, Smart Materials and Structures, and other leading Scopus-indexed engineering journals. His achievements have earned Him academic honors, recognition for innovative piezoelectric actuator development, and respect within research communities through active participation in scholarly networks and contributions to multi-institutional projects. With more than five hundred citations, consistent publication impact, and ongoing collaborations with esteemed researchers from major robotics laboratories, Assoc. Prof. Dr. Qingbing Chang continues to advance the field through high-quality scholarship and engineering innovation. His future trajectory remains strongly oriented toward developing next-generation micro–nano robotic devices, intelligent sensing-actuation platforms, and cross-disciplinary robotic systems that integrate precision engineering, materials science, and artificial intelligence, positioning Him as an influential contributor to future breakthroughs in advanced mechatronic and robotic technologies.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. Piezo robotic hand for motion manipulation from micro to macro. (2023). Citation Count: 118.

  2. Design of a precise linear-rotary positioning stage for optical focusing based on the stick-slip mechanism. (2022). Citation Count: 62.

  3. Development of a novel two-DOF piezo-driven fast steering mirror with high stiffness and good decoupling characteristic. (2021). Citation Count: 60.

  4. A simplified inchworm rotary piezoelectric actuator inspired by finger twist: design, modeling, and experimental evaluation. (2023). Citation Count: 55.

  5. Development of a low capacitance two-axis piezoelectric tilting mirror used for optical assisted micromanipulation. (2021). Citation Count: 49.

 

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.