Overview

I have a long-standing interest in studying machine learning algorithms, tool development, and data analysis in bioinformatics. My academic training and research experience have provided me with an excellent background in multiple disciplines, including computer science, statistics, and computational biology. My major research interests are machine learning and deep learning modeling single cell and spatial omics, especially exploring cell organizations in complex diseases and their translational medicine applications, such as chronic kidney disease, cancer, cardiology, and neurodegenerative disease. My recent works include: a graph neural network based model for both imputation and cell type clustering tasks for single-cell data analysis (Wang et al. Nature Communications 2021), a dimension-agnostic model that identifies spatially variable genes (Wang et al. Nature Communications 2023), and a graph neural network-based model to explore cellular community motifs bridging cell organization and phenotypes (Wang et al. Nature Communications 2025). I also proposed a graph neural network-based model to learn long-range interactions in proteins from molecular dynamics simulations (Zhu et al. Nature Communications 2022). I have special research interests in kidney disease research, which includes relation equivariant graph neural networks for heterogeneous kidney spatial transcriptomics data analysis (Raina et al., Bioinformatics, 2025). In addition, I developed the Fused Partial Gromov- Wasserstein (FPGW) algorithm, a theoretically guaranteed semi-metric Optimal Transport approach modeling spatiotemporal dynamics. Besides method development, I also developed an auto-updating web service to annotate genomic variants on protein structures (Wang et al., Bioinformatics, 2018). It is now an official API to support cBioPortal for cancer genomics which has 4,000 visits per day. I also lead developing NRIMD, a deep learning-based webserver for protein allosteric analysis deployed on a hybrid cloud. This work is highlighted as a cover story in the Journal of Chemical Informatics and Modeling. I was a peer reviewer for more than 20 peer-reviewed journals and conferences, including major journals Science, Nature Cell Biology, Nature Biomedical Engineering, Nature Computational Science, and Nature Communications. I also co-organized three special issues as a guest editor, served as a PC member of several conferences and editors for several peer-reviewed journals. Besides research activities, I actively participated in organizing tutorials in ISMB 2024 and 2026 and symposiums disseminating cutting-edge AI and bioinformatics tools to biological and medical researchers.

Education

• Research Scientist, Xu Lab and Joshi Lab, University of Missouri (2018-2022)
• Postdoctoral Research Fellow, Xu Lab, University of Missouri (2016-2018)
• PhD, Computer Science, Bioinformatics, Jilin University, China (2010-2016)
• Bachelor of Computer Science and Technology, Beijing University of Technology, China (2005)