My research is highly interdisciplinary. My methodological research focuses on statistical network analysis, graph deep learning, causal inference, machine learning, and Riemannian geometry. I modeled the generating process of a network from both non-parametric (e.g., graphon model) and parametric (e.g., SBM) perspectives. I have developed various methods, including network cross-validation, network sampling, network ANOVA, and graphon convolutional network.

I also work closely with biophysicists, engineers, computer scientists, political scientists, public health scientists, and sociologists to solve scientific problems arising from various disciplines. (1) Single-molecule and nanotechnology research. We analyzed single-molecule force spectroscopy data to reveal the binding modes in intermolecular analysis. The proposed method paves a revolutionary path to the massive production and fully automated system for precise intermolecular analysis, such as the interaction between transcription factors and DNA. (2) Political science research. We analyzed how the transnational advocacy network simultaneously provides social power and exacerbates global inequalities. (3) Smart grid research. We applied network methods to detect and localize anomalies in smart grids. (4) Public health and Bioinformatics research. I developed various methods to promote data analytics in gastric cancer, obstructive sleep apnea, and coronary heart disease. Recently, I have been particularly interested in developing methods for spatial transcriptomics, including spatial domain segmentation. (5) Smart cities research. We analyzed transportation networks to promote the smart city.