Fumin Ma | Big data processing | Best Academic Researcher Award

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Prof. Fumin Ma | Big data processing | Best Academic Researcher Award

Vice Dean at Nanjing University of Finance and Economics, China

Fumin Ma is a distinguished professor at the College of Information Engineering, Nanjing University of Finance and Economics. She has made significant contributions to the fields of big data processing, intelligent information processing, and system engineering. With an extensive academic and research career, she has mentored numerous graduate students and played a pivotal role in advancing innovative computational methods. Her research focuses on clustering analysis, knowledge acquisition, cross-modal retrieval, and networked manufacturing. She has received multiple awards and recognitions for her work in academia and research, solidifying her reputation as a leading expert in her domain.

Profile

Scopus

Education

Fumin Ma has a strong academic foundation in system engineering and computer science. She earned her Ph.D. in System Engineering, specializing in Intelligent Information Processing and Intelligent Manufacturing Systems, from Tongji University in 2008. Prior to that, she obtained a Master’s degree in Computer Measurement and Control from the Graduate University of the Chinese Academy of Sciences in 2005 and a Bachelor’s degree in Automation from Henan University in 2002. Her education laid the groundwork for her extensive research in big data and artificial intelligence-driven information processing.

Experience

Dr. Fumin Ma has held several prestigious academic positions throughout her career. She has been a Professor at the College of Information Engineering, Nanjing University of Finance and Economics, since 2018 and currently serves as the Dean of the Computer Science and Technology Department. From 2011 to 2018, she worked as an Associate Professor at the same institution, mentoring graduate students and leading various research projects. Additionally, she was a visiting scholar at University College Dublin, Ireland, from 2014 to 2015. Her experience extends beyond academia as she actively contributes to national and provincial research projects in China.

Research Interests

Dr. Ma’s research is centered around big data processing, intelligent information systems, and system engineering. Her expertise includes clustering analysis, knowledge acquisition, granular computing, and neural networks. She also explores the integration of fuzzy systems, rough sets, and networked manufacturing for optimizing industrial processes. Her research has led to significant advancements in process system modeling, cross-modal retrieval techniques, and energy efficiency assessment methodologies, making her a key figure in the field of intelligent information processing.

Awards and Honors

Dr. Ma has been recognized with numerous awards and honors for her contributions to academia and research. She has been instrumental in the development of a National First-Class Undergraduate Course and was selected as a Middle-aged and Young Academic Leader in the Qinglan Project of Jiangsu Province. She has also received the Second Prize for Postgraduate Education Reform Achievements in Jiangsu Province and the First Prize for Teaching and Research Achievements from the China Education Development Society. Her work continues to influence and shape educational and research frameworks in computer science and engineering.

Publications

Dr. Ma has published extensively in top-tier journals and conferences. Some of her key publications include:

Ma, F., et al. (2024). Key Grids Based Batch-incremental CLIQUE Clustering Algorithm Considering Cluster Structure Changes. Information Sciences, 660, 120109.

Yang, F., Han, M., Ma, F.*, et al. (2024). Disperse Asymmetric Subspace Relation Hashing for Cross-Modal Retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 34(1), 603-617.

Zhang, T., Zhang, Y., Ma, F.*, et al. (2024). Local Boundary Fuzzified Rough K-means Based Information Granulation Algorithm Under the Principle of Justifiable Granularity. IEEE Transactions on Cybernetics, 54(1), 519-532.

Zhang, T., Ma, F.*, et al. (2020). Interval Type-2 Fuzzy Local Enhancement Based Rough K-means Clustering Considering Imbalanced Clusters. IEEE Transactions on Fuzzy Systems, 28(9), 1925-1939.

Ma, F.*, et al. (2019). Compressed Binary Discernibility Matrix Based Incremental Attribute Reduction Algorithm for Group Dynamic Data. Neurocomputing, 344, 20-27.

Zhang, T., Lv, C., Ma, F.*, et al. (2020). A Photovoltaic Power Forecasting Model Based on Dendritic Neuron Networks with the Aid of Wavelet Transform. Neurocomputing, 397(15), 438-446.

Ma, F.*, et al. (2024). Grid Density Peak Clustering Algorithm Based on Zipf Distribution. Control and Decision Making, 39(2), 577-587.

Conclusion

Dr. Fumin Ma’s contributions to academia and research in the field of intelligent information processing and big data analytics have been substantial. Through her extensive research, teaching, and leadership roles, she continues to shape the future of data science and system engineering. Her numerous awards, research projects, and influential publications underscore her impact on both theoretical advancements and practical applications in computational intelligence. As a dedicated scholar and educator, she remains committed to driving innovation and fostering the next generation of researchers in her field.

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

Orcid

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.