Jiyang Dong is a Professor and doctoral supervisor at the School of Electronic Science and Engineering, Xiamen University. He also serves as a Professor and doctoral supervisor at the School of Medicine, Xiamen University, and the National Institute for Data Science in Health and Medicine. He received his Ph.D. from Xiamen University in 2001 and subsequently conducted postdoctoral research at Xidian University under the supervision of Professor Zheng Bao, an Academician of the Chinese Academy of Sciences. Since January 2004, he has been a faculty member at Xiamen University. During his academic career, he has also conducted visiting research at the University of Cambridge, the University of Washington, and Hong Kong Baptist University. These experiences have contributed to his interdisciplinary research background spanning electronic information, physics, analytical chemistry, mass spectrometry, and biomedical data analysis.

Professor Dong's research interests include machine learning, spatial multi-omics, computational mass spectrometry, mass spectrometry imaging, and metabolic network topological analysis. Leading his research team, he has developed methods for metabolic network analysis, spatial metabolomics interpretation, mass spectrometry imaging data processing, omics data integration, and machine learning-based modeling. These studies focus on major diseases such as Alzheimer’s disease, hepatocellular carcinoma, diabetic kidney disease, nasopharyngeal carcinoma, and gastric cancer. His work aims to characterize metabolic dysregulation, spatial heterogeneity, and molecular interactions during disease development, and to extract biologically meaningful and clinically relevant information from complex omics data.

In recent years, Professor Dong has led multiple research projects funded by the National Natural Science Foundation of China and provincial research programs in Fujian. He has published more than 160 peer-reviewed papers in leading journals, including Angewandte Chemie International Edition, Advanced Science, Analytical Chemistry, Analytica Chimica Acta, and Briefings in Bioinformatics. His research focuses on biomedical signals and omics data, using machine learning, network analysis, and multimodal data integration as major methodological tools. Oriented toward the identification of metabolic abnormalities, the analysis of spatial heterogeneity, and the study of molecular mechanisms in major diseases, his work is dedicated to developing interpretable, verifiable, and practically useful methods for biomedical data analysis.