Rate My Professor Guanghua Xiao

Xiao's research focuses on artificial intelligence and deep learning, deep learning in image analysis, spatial molecular profiling, statistical methods in spatial and image analysis, machine learning, spatial statistics, and high-dimensional data analysis. His laboratory develops user-friendly software, computational algorithms, and bioinformatics tools for genomic and imaging data analysis, including AI methods for pathology imaging analysis, tumor region detection, cell spatial organization, and patient outcome prediction from big data. He serves as principal investigator or multiple principal investigator on grants from the National Cancer Institute, National Institute of General Medical Sciences, National Institute of Dental and Craniofacial Research, and Cancer Prevention and Research Institute of Texas. Key publications include "Akt-mediated regulation of autophagy and tumorigenesis through Beclin 1 phosphorylation" (Science, 2012), "Cell-free formation of RNA granules: bound RNAs identify features and components of cellular assemblies" (Cell, 2012), "The origin of hepatocellular carcinoma depends on metabolic zonation" (Science, 2026), "3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network" (Briefings in Bioinformatics, 2026), and "A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records" (JAMIA Open, 2025). His research has been cited over 19,000 times. Honors include Delta Omega National Society Pi Chapter (2006), Merck Doctoral Fellowship (2005), New Student Scholarship (2004) from the University of Minnesota Division of Biostatistics, and various distinctions from Tsinghua University (1997).