Chengyuan Zhang, Hunan University, China
Dr. Chengyuan Zhang, born in October 1985 in Suining County, Hunan, is an Associate Professor and Ph.D. Supervisor at the College of Computer Science and Electronic Engineering, Hunan University. He also serves as a Guest Professor at the College of Information and Intelligence, Hunan Agricultural University. Dr. Zhang earned his Ph.D. in Computer Science from the University of New South Wales, where he also completed his master’s studies under the guidance of Professors Xuemin Lin and Wenjie Zhang. His professional experience includes academic roles in both China and Australia, notably as a postdoctoral researcher. With nearly 40 SCI-indexed publications and over 1,300 citations, he is widely recognized in the domains of multimedia computing, spatio-temporal data analysis, and machine learning. Dr. Zhang is a frequent reviewer for top-tier journals and has contributed to numerous national and provincial-level research projects. He holds editorial and committee roles in several internationally recognized journals and conferences.
Publication Profile
scopus
Education
Dr. Chengyuan Zhang began his academic journey in Software Engineering at Sun Yat-sen University, earning his bachelor’s degree in 2008. He then advanced to the University of New South Wales (UNSW), one of Australia’s leading research institutions, where he obtained a master’s degree in Computer Science in 2011. His academic excellence led him to pursue a Ph.D. in Computer Science at UNSW from July 2011 to December 2015. During his doctoral studies, he worked under the mentorship of esteemed professors Xuemin Lin and Wenjie Zhang. His research during this period laid the groundwork for his future academic contributions in data mining, multimedia computing, and spatio-temporal data processing. The international education experience at UNSW not only honed his technical expertise but also equipped him with a global research perspective and collaborative mindset, helping him transition smoothly into high-impact academic roles upon returning to China.
Experience
Dr. Chengyuan Zhang currently holds the position of Associate Professor and Deputy Director in the Department of Computer Science at Hunan University. He joined the university in December 2019 and assumed his deputy directorship in November 2023. Previously, he served as a Lecturer at the College of Information Science and Engineering at Central South University from 2016 to 2019. His international experience includes a one-year postdoctoral research position at the University of New South Wales from 2015 to 2016, where he continued his research on large-scale data processing. Over the years, Dr. Zhang has participated in and led various nationally funded projects, especially in multimedia, graph data analysis, and AI-driven spatio-temporal applications. He also contributes significantly to the academic community as an editor and reviewer for top journals such as IEEE TKDE, ACM TOIS, and IEEE TNNLS. His career reflects a balanced combination of research innovation, teaching, and academic leadership.
Awards and Honors
Dr. Chengyuan Zhang has received several recognitions for his scholarly contributions, including the Outstanding Reviewer Award from Pattern Recognition Letters, which highlights his dedication to academic quality and peer review. He serves as Academic Editor for Advances in Multimedia and as a Guest Editor for prestigious journals such as Multimedia Tools and Applications and Mathematics. Dr. Zhang has also contributed as a Reviewer for top-tier academic journals including IEEE TPAMI, IEEE TNNLS, IEEE TKDE, ACM TOIS, and ACM TOMM. In addition, he has served as a Program Committee Member for leading international conferences such as ACM Multimedia and IJCAI-PRICAI. These roles are a testament to his academic credibility and recognition within the global research community. His grant-winning research and participation in national foundations reflect his active role in shaping future advancements in multimedia, data mining, and artificial intelligence.
Research Focus
Dr. Chengyuan Zhang’s research centers on multimedia computing, spatio-temporal multi-modal data analysis, image processing, graph data analysis, and machine learning. His work aims to address complex challenges related to information retrieval, representation learning, and knowledge discovery from large-scale, heterogeneous datasets. Specifically, he focuses on designing efficient algorithms for spatio-temporal queries, cross-modal hashing retrieval, and dynamic image enhancement techniques. His research often integrates deep learning, graph theory, and semantic correlation mining, contributing to both theoretical advancements and real-world applications—especially in areas like intelligent agriculture, social recommendation systems, and wireless sensor networks. With support from multiple National Natural Science Foundation of China (NSFC) grants and Hunan provincial research programs, his work is recognized as both innovative and impactful. He has published nearly 40 papers in top journals and conferences such as IEEE TKDE, ACM TOIS, and ACM TOMM, earning over 1,300 citations and an H-index of 18.
Publication Top Notes
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📖 MvHAAN: Multi-view hierarchical attention adversarial network for person re-identification – World Wide Web, 2024
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📖 Bi-Direction Label-Guided Semantic Enhancement for Cross-Modal Hashing – IEEE TCSVT, 2024
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🖼 Using CNN with Multi-Level Information Fusion for Image Denoising – Electronics, 2023
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🖼 Adaptive Dynamic Shuffle Convolutional Parallel Network for Image Super-Resolution – Electronics, 2024
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📷 Efficient Feature Redundancy Reduction for Image Denoising – World Wide Web, 2024
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🔍 Efficient Maximal Biclique Enumeration on Large Uncertain Bipartite Graphs – IEEE TKDE, 2023
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🔍 Efficient Maximum Edge-Weighted Biclique Search on Large Bipartite Graphs – IEEE TKDE, 2022
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🤖 Robust Sparse Weighted Classification for Crowdsourcing – IEEE TKDE, 2022
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🌐 Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation – ACM TOIS, 2022
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🌐 Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs – ACM TOIS, 2022