Xiaodong Feng | Robotics | Best Researcher Award

Prof. Xiaodong Feng | Robotics | Best Researcher Award

Prof. Xiaodong Feng at Shaoxing University, China

Feng Xiaodong, born in June 1987, is an Associate Professor at the School of Civil Engineering, Shaoxing University of Arts and Sciences. Recognized as one of the first leading talents in Zhejiang Province’s 5246 Talent Project and a young talent under Shaoxing’s Special Branch Plan, he specializes in structural engineering. With an international academic footprint and a strong background in intelligent structural systems, Dr. Feng has led numerous national-level projects and published extensively in high-impact journals. His work integrates innovative structural design with smart technologies, contributing significantly to the advancement of flexible and adaptive engineering solutions. 🌉📚🤖

Publication Profile

Scopus

Academic Background

Feng Xiaodong completed his undergraduate studies in Civil Engineering at Central South University (2006–2010), followed by a master’s in Solid Mechanics (2010–2012) and a Ph.D. in Structural Engineering (2012–2016), under Professor Guo Shaohua. He then pursued postdoctoral research at Zhejiang University (2018–2020) with Professor Luo Yaozhi. His academic training spans mechanics, structural analysis, and intelligent systems, providing a robust foundation for his interdisciplinary research. 📘🎓🔬

Professional Experience

Dr. Feng began his academic career at Shaoxing University of Arts and Sciences in 2016 as a Lecturer, advancing to Associate Professor and Laboratory Director in 2021. He also served as a visiting scholar at Kyoto University, Japan (2022–2023). Throughout his career, he has led key research initiatives, mentored students, and collaborated with both academic and industrial partners on advanced structural systems. His experience bridges practical engineering applications and cutting-edge research. 🏢👨‍🏫🌍

Awards and Honors

Feng Xiaodong has received multiple prestigious awards, including two First Prizes and two Second Prizes from the China Steel Structure Association for technological innovation and scientific progress in large-span structures. He also received honors from the Invention and Entrepreneurship Award and Zhejiang Province. These accolades recognize his contributions to the development, design, and digital construction of complex spatial structures, as well as intelligent construction technologies. His pioneering work in structural mechanics and smart infrastructure has earned both national and regional acclaim. 🏆🏗️

Research Focus

Dr. Feng’s research revolves around flexible, movable, and intelligent structures, integrating AI and machine learning for structural design and health monitoring. His key interests include tensegrity structures, prefabricated systems, large-span spatial structures, and structural dynamics. He also focuses on collaborative structural-material design and building industrialization. His interdisciplinary approach combines theoretical innovation with practical applications, aimed at advancing the construction industry’s automation and intelligence. 🤖🧠🏗️📊

Publication Top Notes

📄 Vibration control and robustness analysis of tensegrity structures via fuzzy dynamic sliding mode control method
🗓️ Year: 2024 | 📚 Journal: Structures | 📊 Cited by: 3

📄 Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection
🗓️ Year: 2024 | 📚 Journal: Applied Intelligence | 📊 Cited by: 1

📄 Structural-topic aware deep neural networks for information cascade prediction
🗓️ Year: 2024 | 📚 Journal: PeerJ Computer Science | 📊 Cited by: 1

Conclusion

Dr. Feng Xiaodong, an Associate Professor at Shaoxing University of Arts and Sciences, is a nationally recognized expert in Structural Engineering, specializing in intelligent structures, AI-integrated design, and prefabricated construction technologies. With over 20 peer-reviewed publications in leading journals such as Soft Robotics, Structures, and Structural Control & Health Monitoring, he demonstrates consistent research excellence. As the principal investigator of numerous national and regional research projects, and a recipient of multiple high-level awards from the China Steel Structure Association and Zhejiang Province, Dr. Feng has shown both academic leadership and practical innovation. His international exposure as a Visiting Scholar at Kyoto University and his role in training future engineers across disciplines further underscore his qualifications. Dr. Feng’s contributions significantly advance the field of intelligent structural systems and make him an outstanding candidate for a Best Researcher Award.

 

 

Wenwei Luo | Automation | Best Researcher Award

Mr. Wenwei Luo | Automation | Best Researcher Award

Mr. Wenwei Luo at Institute of Logistics Science and Engineering, Shanghai Maritime University, China

Wenwei Luo is a passionate robotics researcher pursuing his Master’s degree at Shanghai Maritime University, specializing in control science and engineering. With a strong foundation in robotics engineering from Zhejiang Normal University, he has demonstrated academic excellence, technical proficiency, and innovative thinking in reinforcement learning and evolutionary robotics. Wenwei has published impactful research, led interdisciplinary projects, and earned recognition in national competitions. He possesses a unique combination of embedded systems expertise and AI-based control strategies, positioning him as a rising talent in intelligent robotics. His vision is to bridge adaptive learning and real-world robotics for autonomous systems. 🤖📚🔍

Publication Profile

Orcid

Academic Background

Wenwei Luo is currently pursuing a Master’s degree in Control Science and Engineering at Shanghai Maritime University under Associate Professor Bo Li, with a GPA of 3.84/4.0 and double First Academic Scholarships. His research interests span reinforcement learning, adaptive control, and evolutionary robotics. Previously, he earned his Bachelor’s degree in Robotics Engineering from Zhejiang Normal University under Associate Professor Hu Lan, graduating with a GPA of 3.40/4.0 and receiving a Third Academic Scholarship. Wenwei’s academic background blends strong theoretical knowledge with hands-on experience in intelligent systems and control engineering. 🧠🎓📈

Professional Experience

Wenwei has led and contributed to various high-impact robotics projects. As Principal Investigator, he developed a novel inner-outer loop framework for modular robots using reinforcement learning and evolutionary optimization. As Co-Investigator, he worked on intelligent drone navigation and pursuit-evasion for port defense. He also led a RoboMaster project, designing embedded software for a wheeled robot with Mecanum wheels and a shooting mechanism. His work integrates control algorithms, real-time systems, and AI-based decision-making, validated through both simulations and real-world experiments. His diverse project roles highlight both leadership and deep technical acumen. 🤖🧪🧑‍🔬

Awards and Honors

Wenwei has received several prestigious awards and honors throughout his academic career. At Shanghai Maritime University, he won the Third Prize in the 2022 “Huawei Cup” China Post-Graduate Mathematical Contest in Modeling. During his undergraduate years, he received the National Third Prize in the 2021 National College Students Robotics Competition (RoboMaster Event). He has also been awarded the First Academic Scholarship twice during his master’s program and the Third Academic Scholarship during his bachelor’s. These recognitions reflect his commitment to excellence and contributions to engineering and robotics research. 🥇🎖️📜

Research Focus

Wenwei’s research centers on intelligent control and adaptive robotics, specifically focusing on reinforcement learning-based control, evolutionary robotics, and adaptive dynamic programming. He has pioneered a hierarchical framework integrating genetic algorithms and deep RL (PPO) for optimizing morphology and control of modular robots. His work extends to autonomous UAV path planning and pursuit-evasion strategies using fuzzy logic, neural networks, and Lyapunov-based verification. His research leverages advanced tools such as JAX and GPU parallelism for real-time learning and optimization. Wenwei aims to develop scalable, autonomous systems capable of intelligent behavior in complex environments. 🧠📡🚀

Publication Top Notes

📄  Inner–Outer Loop Intelligent Morphology Optimization and Pursuit–Evasion Control for Space Modular Robot

 📅Year: 2025 | 📚 Journal: Actuators, Volume 14

Conclusion

Wenwei Luo is a highly promising early-career researcher whose academic excellence, innovative research, and practical contributions make him a strong contender for a Best Researcher Award. With a Master’s GPA of 3.84/4.0 and a strong undergraduate foundation, he has demonstrated consistent academic achievement. His research focuses on cutting-edge areas such as modular robotics, reinforcement learning, and evolutionary optimization, exemplified by his novel inner–outer loop architecture combining genetic algorithms and PPO for pursuit–evasion tasks. He has authored peer-reviewed publications, including a journal article in Actuators, and holds a patent alongside software copyrights, reflecting both theoretical and applied innovation. His technical skill set spans AI frameworks, embedded systems, and robotics platforms, and his leadership roles in multiple projects showcase his capability for independent and collaborative research. Combined with national competition awards and scholarships, Luo’s profile embodies the qualities celebrated by the Best Researcher Award.

 

HaiTian Chen | Computer Science | Best Researcher Award

Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

Profile

ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]