Keun Chang Kwak | Robotics and Automation | Best Researcher Award

Published on by Scientific World Research Awards

Prof. Keun Chang Kwak | Robotics and Automation | Best Researcher Award

Professor | Chosun University | South Korea

Professor Keun-Chang Kwak is a distinguished researcher in the fields of computational intelligence, biometrics, and robotic vision systems, with extensive expertise in granular and neuro-fuzzy modeling, face and speaker recognition, knowledge extraction, behavior recognition, and auditory signal processing. He earned his Ph.D. in Electrical Engineering from Chungbuk National University, Korea, in 2002, following an MS in 1998 and a BS in 1996 from the same institution. Over his career, Prof. Kwak has held several prominent positions, including Professor at Chosun University, Korea (2007–present), Visiting Professor at California State University Fullerton, USA (2014–2015), Senior Researcher at the Intelligent Robot Research Division, Electronics and Telecommunications Research Institute (ETRI), Korea (2005–2007), and Postdoctoral Fellowships at the University of Alberta, Canada (2003–2005) and Chungbuk National University, Korea (2002–2003). He has also served as Project Manager of the AI Convergence University Project Division (2021–present) and Vice Director of the National Center of Excellence in Software at Chosun University (2018–2020), leading numerous national and international research initiatives. His research interests include computational intelligence, deep learning, speech emotion recognition, ECG-based biometrics, human-robot interaction, and knowledge extraction using fuzzy clustering. Prof. Kwak’s prolific publication record includes 138 Scopus-indexed documents, 1,667 citations, and an h-index of 21, reflecting high-impact contributions to journals such as IEEE Access, Applied Sciences, Electronics, and Sensors. He has led and mentored research teams, collaborated internationally, and contributed significantly to the robotics and AI communities. Prof. Kwak’s achievements are recognized through multiple awards, leadership roles, and professional memberships, highlighting his influence on research, education, and technology advancement. His work demonstrates exceptional innovation, academic excellence, and the potential to drive future breakthroughs in AI, robotics, and computational intelligence, making him highly deserving of the Best Researcher Award.

Profiles: Scopus | Google Scholar

Featured Publications

  1. Pedrycz, W., & Kwak, K. C. (2006). Linguistic models as a framework of user-centric system modeling. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(2), 187–200. [Citations: 187]

  2. Kwak, K. C., & Pedrycz, W. (2005). Face recognition using a fuzzy fisherface classifier. Pattern Recognition, 38(10), 1717–1732. [Citations: 185]

  3. Kwak, K. C., & Pedrycz, W. (2007). Face recognition using an enhanced independent component analysis approach. IEEE Transactions on Neural Networks, 18(2), 530–541. [Citations: 167]

  4. Byeon, Y. H., Pan, S. B., & Kwak, K. C. (2019). Intelligent deep models based on scalograms of electrocardiogram signals for biometrics. Sensors, 19(4), 935. [Citations: 138]

  5. Kwak, K. C., & Pedrycz, W. (2005). Face recognition: A study in information fusion using fuzzy integral. Pattern Recognition Letters, 26(6), 719–733. [Citations: 112]

 

Jiatao Ding | Robotics and Automation | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Jiatao Ding | Robotics and Automation | Best Researcher Award

Postdoctoral Researcher | University of Trento | Italy

Dr. Jiatao Ding is an accomplished robotics researcher whose work focuses on optimal control, robot learning, and legged robotics, with a strong record of international collaborations and impactful scientific contributions. He obtained his Bachelor’s degree in Mechanical Engineering from Wuhan University in 2014 (Cum Laude), followed by a Doctorate in Mechatronics Engineering from Wuhan University in 2020, during which he also served as a Ph.D. Fellow at the Italian Institute of Technology (2018–2020), gaining valuable international exposure. Professionally, Dr. Ding has held prestigious research appointments including Research Assistant Scientist at the Chinese University of Hong Kong (2020–2022), Postdoctoral Researcher at Delft University of Technology (2022–2025), and currently, Postdoctoral Researcher at the University of Trento, Italy (2025–present). His research interests lie in humanoid and quadruped locomotion, reinforcement learning, and bio-inspired robotic control, where he has actively contributed to major EU H2020 projects such as Inverse, Nature Intelligence, and CogIMon, along with NSFC-funded projects in China. Dr. Ding’s research skills span advanced reinforcement learning, trajectory optimization, hierarchical and model predictive control, and adaptive locomotion strategies, which have enabled breakthroughs in versatile bipedal and quadrupedal robotic systems. His scholarly output is extensive, with publications in flagship robotics venues such as IEEE ICRA, IROS, IEEE Transactions on Robotics, IEEE/ASME Transactions on Mechatronics, and Advanced Robotics, reflecting both quality and global reach. He has served the academic community as a reviewer for leading journals and conferences, session chair at AIM 2025, associate editor at UR 2025, and guest editor for special issues in reputed journals, demonstrating leadership and commitment to advancing robotics research. His awards and honors include invited talks, editorial board appointments, and recognition through collaborative project leadership across Europe and Asia. According to Scopus, Dr. Ding has achieved 262 citations across 241 documents with an h-index of 11, underscoring both productivity and research impact. In conclusion, Dr. Jiatao Ding exemplifies an emerging global leader in robotics whose academic excellence, technical expertise, and dedication to collaborative research position him strongly for future innovations in intelligent robotic systems, making him a deserving candidate for international recognition.

Profile: Google Scholar

Featured Publications

Atanassov, V., Ding, J., Kober, J., Havoutis, I., & Della Santina, C. (2024). Curriculum-based reinforcement learning for quadrupedal jumping: A reference-free design. IEEE Robotics & Automation Magazine, 32(2), 35–48. Citations: 24

Ding, J., Han, L., Ge, L., Liu, Y., & Pang, J. (2022). Robust locomotion exploiting multiple balance strategies: An observer-based cascaded model predictive control approach. IEEE/ASME Transactions on Mechatronics, 27(4), 2089–2097. Citations: 24

Ding, J., Wang, Y., Yang, M., & Xiao, X. (2018). Walking stabilization control for humanoid robots on unknown slope based on walking sequences adjustment. Journal of Intelligent & Robotic Systems, 90(3), 323–338. Citations: 16

Ding, J., Zhou, C., Xin, S., Xiao, X., & Tsagarakis, N. G. (2021). Nonlinear model predictive control for robust bipedal locomotion: Exploring angular momentum and CoM height changes. Advanced Robotics, 35(18), 1079–1097. Citations: 26*

Ding, J., Atanassov, V., Panichi, E., Kober, J., & Della Santina, C. (2024). Robust quadrupedal jumping with impact-aware landing: Exploiting parallel elasticity. IEEE Transactions on Robotics, 40(1), 3212–3231. Citations: 13