Fumin Ma | Big data processing | Best Academic Researcher Award

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.

Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

Dr. Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

Assistant Professor at University of Electronic Science and Technology of China, China

Dr. Ali Nawaz Sanjrani is a dedicated academician and scholar with over 18 years of interdisciplinary experience spanning research, teaching, and industrial project management. His expertise lies in reliability engineering, quality control, health and safety management, and complex machine diagnostics. As a professional with a strong commitment to excellence, Dr. Sanjrani has made significant contributions to engineering education and industrial advancements. His research primarily focuses on reliability monitoring, fault diagnosis, and the application of machine learning in predictive maintenance.

Profile

Orcid

Education

Dr. Sanjrani earned his Ph.D. in Mechanical Engineering from the University of Electronics Science and Technology, Chengdu, China, specializing in reliability monitoring, diagnostics, and prognostics of complex machinery. His doctoral coursework included advanced subjects such as Computer-Aided Manufacturing (CAM), Operations Research (OR), Reliability & Quality Engineering, Automation & Controls, and Finite Element Analysis (FEA). Prior to this, he completed his Master’s degree in Industrial Manufacturing Engineering from NED University of Engineering & Technology, Karachi, with a focus on lean manufacturing. He holds a Bachelor’s degree in Mechanical Engineering from QUEST, Nawabshah, where he developed a strong foundation in mechanical manufacturing and materials engineering.

Experience

Dr. Sanjrani has held several academic and industrial positions, reflecting his diverse skill set and leadership abilities. He served as an Assistant Professor at Mehran University of Engineering and Technology, SZAB Campus, from 2016 to 2020, where he was actively involved in teaching, research, and mentoring students. Additionally, he worked as a visiting faculty member at Indus University, Karachi. His industrial experience includes working as a Quality Assurance Engineer at Descon Engineering Works Limited, Lahore, where he managed quality control processes and implemented international quality management standards.

Research Interests

Dr. Sanjrani’s research interests are centered around machine learning applications in fault diagnosis and predictive maintenance, reliability analysis, and quality engineering. His work integrates artificial intelligence-driven methodologies to enhance the reliability and operational efficiency of high-speed train bearings, microgrids, and other complex mechanical systems. His research also extends to fluid dynamics, heat transfer, and smart manufacturing processes, emphasizing innovative approaches to industrial problem-solving.

Awards and Recognitions

Dr. Sanjrani has been recognized for his academic and research excellence through several prestigious awards. In 2024, he won the 3rd Prize for Academic Excellence at the University of Electronics Science and Technology, China. Additionally, he received the 3rd Prize for Performance Excellence at the same institution. He was also awarded the fully funded Chinese Government Scholarship (CSC) in 2020 for his Ph.D. studies. His industrial contributions have been acknowledged with appreciation certificates from Karachi Shipyard & Engineering Works (KSEW) for achieving multiple international certifications and successful project implementations.

Selected Publications

Sanajrani, A. N. (2025). “High-Speed Train Bearing Health Assessment Based on Degradation Stages Through Diagnosis and Prognosis by Using Dual-Task LSTM With Attention Mechanism.” Quality and Reliability Engineering International Journal, Wiley. DOI: https://doi.org/10.1002/qre.3757

Sanajrani, A. N. (2025). “High-Speed Train Wheel Set Bearing Analysis: Practical Approach to Maintenance Between End of Life and Useful Life Extension Assessment.” Results in Engineering, Elsevier. DOI: https://doi.org/10.1016/j.rineng.2024.103696

Sanajrani, A. N. (2025). “Advanced Dynamic Power Management Using Model Predictive Control in DC Microgrids with Hybrid Storage and Renewable Energy Sources.” Journal of Energy Storage, Elsevier. DOI: https://doi.org/10.1016/j.est.2024.114830

Sanajrani, A. N. (2024). “Dynamic Temporal LSTM-Seqtrans for Long Sequence: An Approach for Credit Card and Banking Accounts Fraud Detection in Banking Systems.” 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). DOI: 10.1109/ICCWAMTIP64812.2024.10873619

Sanajrani, A. N. (2024). “High-Speed Train Health Assessment Based on Degradation Stages and Fault Classification Using Dual-Task LSTM with Attention Mechanism.” The 6th International Conference on System Reliability and Safety Engineering. DOI: 10.1109/SRSE63568.2024.10772528 (EI & Scopus Indexed)

Sanajrani, A. N. (2024). “A C-band Sheet Beam Staggered Double Grating Extended Interaction Oscillator.” IEEE International Conference on Plasma Science (ICOPS). DOI: 10.1109/ICOPS58192.2024.10625809 (EI Indexed)

Sanajrani, A. N. (2023). “Bearing Health and Safety Analysis to Improve the Reliability and Efficiency of Horizontal Axis Wind Turbine (HAWT).” ESREL 2023, Southampton, UK (ISBN: 978-981-18-8071-1).

Conclusion

Dr. Ali Nawaz Sanjrani is a distinguished academic and industry professional with a strong research background in reliability engineering, artificial intelligence, and machine learning applications. His work significantly contributes to the fields of predictive maintenance, fault diagnostics, and industrial automation. With a proven record of academic excellence, numerous international publications, and substantial industrial experience, Dr. Sanjrani continues to drive innovation in engineering and technology. His dedication to bridging the gap between academia and industry ensures impactful contributions to the advancement of modern engineering solutions.