Prof. Ji Xu, Guizhou University, China
Ji Xu (M’22) is an associate professor at the State Key Laboratory of Public Big Data, Guizhou University, China. He obtained his B.S. in Computer Science from Beijing Jiaotong University in 2004 and earned his Ph.D. in Computer Science from Southwest Jiaotong University in 2017. With expertise in data mining, granular computing, and machine learning, he has significantly contributed to the field through extensive research and publications. Dr. Xu has authored and co-authored over 30 papers in prestigious international journals, including IEEE TFS, IEEE TCYB, and Information Sciences. He also serves as a reviewer for top-tier journals like IEEE TNNLS, IEEE TFS, and Pattern Recognition. As an active member of IEEE, CCF, and CAAI, he remains at the forefront of technological advancements in artificial intelligence and big data analytics. His work continues to shape the future of intelligent computing and large-scale data processing.
Professional Profile
Google Scholar
Summary of Suitability for the Research for Best Researcher Award
Ji Xu is highly suitable for the “Research for Best Researcher Award” due to his impressive academic and professional achievements in the field of computer science, with a particular focus on data mining, granular computing, and machine learning. His educational background includes a Bachelor’s degree from Beijing Jiaotong University and a Ph.D. from Southwest Jiaotong University, which demonstrate his foundational expertise in these critical fields. As an associate professor at the State Key Laboratory of Public Big Data at Guizhou University, Xu has a clear commitment to advancing research in his area of specialization.
Xu’s research productivity further demonstrates his suitability for the award. He has authored over 30 peer-reviewed papers in prestigious international journals such as IEEE TFS, IEEE TCYB, IEEE JIoT, Information Sciences, and others. His contributions to these journals reflect his high-level expertise and ability to make significant advancements in his field. Furthermore, Xu has co-authored a book, showcasing his ability to synthesize and communicate complex ideas to a broader audience.
🎓 Education
Ji Xu’s academic journey began at Beijing Jiaotong University, where he obtained his Bachelor of Science (B.S.) in Computer Science in 2004. He later pursued advanced studies at Southwest Jiaotong University, earning his Doctor of Philosophy (Ph.D.) in Computer Science in 2017. His doctoral research focused on artificial intelligence, data mining, and computational intelligence, laying a strong foundation for his contributions to big data analytics. Throughout his academic career, he demonstrated exceptional analytical skills and a deep understanding of machine learning techniques. His education provided him with the technical expertise required to explore complex datasets and develop intelligent computing models. Additionally, his training at two leading Chinese universities equipped him with interdisciplinary knowledge in software engineering, algorithms, and large-scale data processing. His academic background remains a cornerstone of his professional research, guiding his work in advanced computational methods and innovative AI applications.
💼 Professional Experience
Dr. Ji Xu is currently an associate professor at the State Key Laboratory of Public Big Data, Guizhou University. In this role, he leads research in big data analytics, machine learning, and granular computing. His professional experience spans academia and research, with a focus on developing intelligent algorithms for large-scale data processing. Over the years, he has collaborated with industry and academia on high-impact projects related to artificial intelligence and computational intelligence. As an active member of IEEE, CCF, and CAAI, he contributes to the global research community through technical publications, conference presentations, and journal reviews. In addition to his research, he mentors graduate students, guiding them in innovative AI and data science projects. His expertise in handling complex data-driven challenges has established him as a prominent researcher in the field. Dr. Xu’s work continues to influence advancements in big data and artificial intelligence applications.
🏅 Awards and Recognition
Dr. Ji Xu has received multiple accolades for his contributions to computer science, particularly in big data analytics, machine learning, and granular computing. He has been recognized for his research excellence through numerous best paper awards at international conferences. His extensive publication record in prestigious journals such as IEEE TFS, IEEE TCYB, and Neurocomputing has earned him a reputation as a leading researcher in artificial intelligence. Additionally, he serves as a reviewer for top-tier journals, including IEEE TNNLS, IEEE TFS, and Pattern Recognition, demonstrating his influence in shaping the field. As a distinguished member of IEEE, CCF, and CAAI, he actively participates in research communities and contributes to major advancements in computational intelligence. His innovative work in data science and AI continues to garner international recognition, positioning him among the top researchers driving the future of intelligent data processing and analytics.
Dr. Ji Xu’s research expertise encompasses data mining, granular computing, and machine learning. His ability to analyze large-scale datasets and develop intelligent algorithms has led to groundbreaking contributions in big data analytics. He specializes in computational intelligence, predictive modeling, and pattern recognition, applying advanced AI techniques to solve complex real-world problems. His skills extend to deep learning, natural language processing (NLP), and algorithm optimization, enabling him to create efficient data-driven solutions. With a strong foundation in mathematical modeling and statistical analysis, he excels in deriving meaningful insights from high-dimensional data. His role as a reviewer for IEEE TFS, IEEE TNNLS, and Pattern Recognition reflects his deep understanding of AI methodologies. Additionally, he collaborates on interdisciplinary projects, integrating AI with emerging technologies such as IoT and edge computing. His research continues to push the boundaries of artificial intelligence, transforming data analytics and intelligent systems.
Publication Top Notes
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DenPEHC: Density peak based efficient hierarchical clustering
Authors: J Xu, G Wang, W Deng
Journal: Information Sciences, 373, 200-218
Citations: 142
Year: 2016
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A survey of smart water quality monitoring system
Authors: J Dong, G Wang, H Yan, J Xu, X Zhang
Journal: Environmental Science and Pollution Research, 22(7), 4893-4906
Citations: 139
Year: 2015
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Granular computing: from granularity optimization to multi-granularity joint problem solving
Authors: G Wang, J Yang, J Xu
Journal: Granular Computing, 2(3), 105-120
Citations: 138
Year: 2017
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Self-training semi-supervised classification based on density peaks of data
Authors: D Wu, M Shang, X Luo, J Xu, H Yan, W Deng, G Wang
Journal: Neurocomputing, 275, 180-191
Citations: 130
Year: 2018
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Review of big data processing based on granular computing
Authors: J Xu, GY Wang, H Yu
Journal: Chinese Journal of Computers, 38(8), 1497-1517
Citations: 59
Year: 2015
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基于粒计算的大数据处理 (Big Data Processing Based on Granular Computing)
Authors: 徐计 (J Xu), 王国胤 (G Wang), 于洪 (H Yu)
Journal: 计算机学报 (Chinese Journal of Computers), 38(8), 1497-1517
Citations: 50
Year: 2015
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Fat node leading tree for data stream clustering with density peaks
Authors: J Xu, G Wang, T Li, W Deng, G Gou
Journal: Knowledge-Based Systems, 120, 99-117
Citations: 44
Year: 2017
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Piecewise two-dimensional normal cloud representation for time-series data mining
Authors: W Deng, G Wang, J Xu
Journal: Information Sciences, 374, 32-50
Citations: 40
Year: 2016
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A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques
Authors: W Deng, G Wang, X Zhang, J Xu, G Li
Journal: Neurocomputing, 173, 1671-1682
Citations: 37
Year: 2016
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Local-Density-Based Optimal Granulation and Manifold Information Granule Description
Authors: J Xu, G Wang, T Li, W Pedrycz
Journal: IEEE Transactions on Cybernetics
Citations: 28
Year: 2017