Dr. Dingjie Wang is a Tenure-track Associate Professor and Researcher in Institute for Math & AI, Wuhan University. He received his Ph.D. in Computational Mathematics from Wuhan University in 2019 under the supervision of Prof. Xiufen Zou. During his doctoral training, he was a visiting scholar at the University of Iowa (2017–2018) and the Ohio State University (2018–2019). From 2019 to 2024, he completed his postdoctoral research at the Ohio State University and the University of Michigan, Ann Arbor, mentored by Prof. Kin Fai Au. Before joining Wuhan University, he served as a Research Fellow in the Department of Applied Mathematics at The Hong Kong Polytechnic University. Dr. Wang currently serves on the Youth Editorial Boards of iMeta (2025 IF = 33.2) and Genomics, Proteomics & Bioinformatics, and has reviewed manuscripts for leading journals such as Nature Biotechnology, Genome Biology, Bioinformatics, and Artificial Intelligence Review.

His research integrates mathematics, machine learning, and biomedical sciences, with a particular focus on bioinformatics, computational systems biology, and tensor-based data analysis. Currently, his primary research interest lies in developing innovative algorithms and statistical models to perform reliable quantitative and function analyses of gene isoforms and Transposable Elements (TEs) using cutting-edge sequencing techniques from PacBio and Oxford Nanopore Technologies. His studies are anticipated to provide an efficient bioinformatics platform for improve our understanding of gene isoforms and TEs with complex biomedical context in a comprehensive manner. His research has been published as first or co-first author in Nature Biotechnology, Nature Methods, Nature Communications, SIAM Journal on Applied Mathematics, Chaos, Information Sciences, Applied Mathematical Modelling, and other leading international journals.

Representative research topics include:

l  Develop statistical models for identifying and quantifying gene isoforms and TEs.

l  Design long-read-based pipeline to reveal landscape of zygotic TE activation during early embryogenesis in zebrafish.

l  Construction of gene/isoform regulatory networks using diverse genomic data.

l  Design network-based computational framework to predict and differentiate functions among gene isoforms.

l  Develop tensor-based indicators to identify key components in complex gene/isoform regulatory networks.

l  Comprehensive multi-omics studies on the giant panda.

Personal homepage: https://dingjie-wang.github.io/online-cv/