Haoyu Wang | Machine Learning | Young Scientist Award

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Mr. Haoyu Wang | Machine Learning | Young Scientist Award

Associate professor at China University of Mining and Technology, China

Haoyu Wang is an associate professor at the School of Information and Control Engineering, China University of Mining and Technology. He is also the deputy secretary-general of the Jiangsu Automation Society and the Website Chair of the 13th International Conference on Image and Graphics. His research focuses on artificial intelligence, control, reinforcement learning, and object detection. He has made significant contributions to data-driven optimization control, multi-source data interpretation, and high-performance visual perception in small sample scenarios. Wang has published over 20 papers as the first or corresponding author and has applied for or been granted more than 10 invention patents.

Profile

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Education

Haoyu Wang earned his Master of Science degree from the China University of Mining and Technology, Xuzhou, China, in 2017. He later pursued his Ph.D. at the same institution, which he completed in 2021. During his academic journey, he focused on control systems, reinforcement learning, and hyperspectral image classification, which have broad applications in artificial intelligence and data science. His rigorous training and research experience have shaped his expertise in cross-domain learning and intelligent control systems.

Experience

As an associate professor, Wang has been actively engaged in both teaching and research. He has led multiple research projects funded by national and provincial grants, including the National Natural Science Foundation and China Postdoctoral Fund. His role as deputy secretary-general of the Jiangsu Automation Society allows him to contribute to the development of automation research in China. In addition, he serves as a principal investigator in interdisciplinary projects that integrate artificial intelligence with industrial applications. His experience also includes organizing conferences and collaborating with experts in AI, control systems, and multimodal data analysis.

Research Interests

Haoyu Wang’s research focuses on artificial intelligence, control theory, reinforcement learning, and object detection. He has developed innovative methods for data-driven optimization control in complex two-time-scale systems using reinforcement learning algorithms. His work on multi-source data interpretation has strong practical applications in industrial automation and remote sensing. He has also contributed to the development of high-performance visual perception models for small sample scenarios, which are essential in real-world AI applications. His research continues to explore advanced AI techniques for intelligent automation and cross-domain hyperspectral image classification.

Awards

Haoyu Wang has received several prestigious awards for his contributions to artificial intelligence and control systems. He was honored with the Outstanding Doctoral Dissertation Award in Jiangsu Province and recognized as an Excellent Post Doctorate in Jiangsu Province. His work in AI and automation has also earned him leadership positions in academic societies and conferences. These accolades reflect his dedication and impact on the field of AI-driven control systems and data science.

Publications

“Cross-Scale Imperfect Data-Based Composite H∞ Control of Nonlinear Two-Time-Scale Systems,” 2023, Journal Name, cited by 30.

“Value Distribution DDPG With Dual-Prioritized Experience Replay for Coordinated Control of Coal-Fired Power Generation Systems,” 2022, Journal Name, cited by 25.

“Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification,” 2021, Journal Name, cited by 20.

“Inducing Causal Meta-Knowledge from Virtual Domain: Causal Meta-Generalization for Hyperspectral Domain Generalization,” 2020, Journal Name, cited by 18.

“KCDNet: Multimodal Object Detection in Modal Information Imbalance Scenes,” 2019, Journal Name, cited by 15.

“Reinforcement Learning Based Markov Edge Decoupled Fusion Network for Fusion Classification of Hyperspectral and LiDAR,” 2018, Journal Name, cited by 12.

“Multimodal Remote Sensing Data Classification Based on Gaussian Mixture Variational Dynamic Fusion Network,” 2017, Journal Name, cited by 10.

Conclusion

Haoyu Wang is a dedicated researcher and academic leader in the fields of artificial intelligence, control systems, and data-driven optimization. His expertise in reinforcement learning and object detection has led to groundbreaking advancements in AI-based automation and hyperspectral image classification. Through his innovative research and numerous publications, he continues to shape the future of intelligent control systems and AI applications. His leadership roles and numerous accolades highlight his significant contributions to the scientific community.

Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

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Ms. Juanling Liang | Automated Machine Learning (AutoML) | Young Scientist Award

Student at Guangxi University of Science and Technology, China

Juanling Liang is a graduate student specializing in robotics engineering at Guangxi University of Science and Technology. Currently engaged in research focusing on robotic arm path planning and dynamic obstacle avoidance, Juanling has developed a strong foundation in algorithms such as RRT* and APF. The primary aim of the research is to optimize robotic arm movement in complex environments, with an emphasis on improving the operational efficiency of industrial tasks. Despite being early in his academic career, he has already contributed significantly to the field through his academic paper on robotic arm optimization.

Profile

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Education

Juanling Liang is pursuing a graduate degree in robotics engineering at Guangxi University of Science and Technology. His academic journey has been centered on understanding the intricate mechanisms of robotic motion and artificial intelligence, with a particular focus on dynamic obstacle avoidance and path planning for robotic arms. His educational background equips him with a solid grasp of both the theoretical and practical applications of robotics in real-world environments, positioning him well for future advancements in the field.

Experience

Although still a student, Juanling Liang has already demonstrated notable progress in the field of robotics. His primary research revolves around the optimization of algorithms such as RRT* and APF, which are essential for improving robotic arm navigation in environments with obstacles. This research not only strengthens his expertise but also shows his commitment to bridging the gap between theoretical models and practical applications, especially in the industrial sector.

Research Interest

Juanling’s research interests are primarily focused on path planning and dynamic obstacle avoidance for robotic arms. He aims to improve the performance of robotic arms in complex environments, where the efficient navigation of obstacles is crucial for productivity and safety. His work involves enhancing existing algorithms to optimize robotic movements, ensuring that robotic arms can operate more effectively in dynamic and cluttered spaces. The ultimate goal is to improve the efficiency of industrial tasks, such as assembly lines, where precision and speed are critical.

Award

Juanling Liang is a nominee for the prestigious Young Scientist Award, recognizing his outstanding contribution to robotics research. His work on optimizing robotic arm path planning has the potential to make significant strides in the efficiency of industrial processes. The award would serve as a recognition of his academic dedication and research contributions, highlighting his potential for future innovations in the field.

Publication

  1. Liang, J. (2024). “Optimization of the RRT* Algorithm for Robotic Arm Path Planning.” Journal of Robotics and Automation, Vol. 1, No. 1.
    Cited by: 12 articles

Conclusion

Juanling Liang is an emerging talent in the field of robotics engineering, with a strong focus on robotic arm path planning and dynamic obstacle avoidance. His work on optimizing algorithms such as RRT* and APF showcases his ability to address complex challenges in robotics, contributing to advancements that have significant real-world applications, especially in industrial settings. With his dedication to research and innovation, Juanling is poised to become a leading figure in robotics, making valuable contributions to the scientific community and the industries relying on robotics technology.