Zhaoxia Duan | Robotics and Automation | Best Researcher Award

Assoc. Prof. Dr. Zhaoxia Duan | Robotics and Automation | Best Researcher Award

Associate Professor | Hohai University | China

Assoc. Prof. Dr. Zhaoxia Duan is a distinguished researcher whose academic journey began with a B.Sc. in Automation (2007-2011) and a Ph.D. in Control Science and Engineering (2011-2017) from Nanjing University of Science and Technology, including a visiting research fellowship at Shibaura University of Technology in Japan (2013-2014). Currently a tenured associate professor at the School of Artificial Intelligence and Automation, Hohai University, she earlier served as a lecturer in the College of Energy and Electrical Engineering (2017-2023) and has also been a postdoctoral researcher at the School of Mathematics, Southeast University since 2019. Her research interests encompass multi-agent network systems, path planning for robots and autonomous underwater vehicles (AUVs), multi-dimensional / 2D system theory and applications, switching system theory, and positive system theory; she has developed strong research skills in stability analysis, finite-frequency control, fuzzy hybrid system modeling, fault detection observers, optimization via machine-learning (e.g. multi-objective PSO + Q-learning), and observer/controller synthesis for 2D positive / Markov jump / delayed / hybrid systems. Over her career, she has led multiple funded projects including national, postdoctoral, and central university grants, supervised research, and contributed to theoretical advances in control and automation. Her publication record is robust, with over 30+ journal articles, two authorized invention patents, and the compilation of a university textbook. She has been recognized with numerous awards and honors such as teaching excellence, thesis supervision awards, lecture competition prizes, and reviewer excellence. According to Scopus metrics, she has authored approximately 64 documents, has received around 710 citations, and has an h-index of 17. In conclusion, Dr. Duan is a well-rounded scholar who bridges rigorous theoretical research with practical intelligent automation applications, and continues to make impactful contributions to control theory, robotics and multi-agent systems.

Profile: Scopus 

Featured Publications

Duan, Z., Zhang, Y., Xu, Z., & Xiang, Z. (2025). Path planning problem in rough terrain by multi-objective PSO algorithm combined with Q-learning and crossover operator. Applied Soft Computing Journal, 184, 113798.

Duan, Z., Fu, Y., Wang, R., & Xiang, Z. (2025). l₁-gain control for delayed 2-D Markov jump positive systems in the Fornasini–Marchesini model. Journal of the Franklin Institute, 362(13), 107907.

Duan, Z., Zhang, Y., Wang, R., Xu, Z., & Xiang, Z. (2025). Robot path planning in rough terrain based on multi-objective crossover-mutation particle swarm optimization. Evolutionary Intelligence, 18, 64.

Duan, Z., Shao, Z., & Xiang, Z. (2025). A collaborative command and allocation model for manned and unmanned maritime forces. Journal of Army Engineering University of PLA, 3(4), 98–104. (In Chinese).

Duan, Z., Chu, C., Ghous, I., & Xiang, Z. (2024). On the l∞-gain of 2-D positive Roesser systems with bounded time-varying delays. Journal of the Franklin Institute, 361, 106819.

 

Jiatao Ding | Robotics and Automation | Best Researcher Award

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