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keywords
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Bioinformatics, Single-cell RNA sequencing, Cancer Immunotherapy, Spatial Transcriptomics
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overview
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My laboratory focuses on leveraging advanced computational methods to explore complex biological systems in diseases and medicine. My research aims to unravel the intricacies of cellular interactions, gene expression patterns, and disease mechanisms using cutting-edge AI techniques, contributing valuable insights to biomedical research and personalized medicine. My research work as a Ph.D. student at The Academy of Mathematics and Systems Science (AMSS) of the Chinese Academy of Sciences (CAS) in Beijing focused on Artificial Neural Network and Optimization with a specialization in Bioinformatics. During my postdoctoral research at Houston Methodist & Weill Cornell Medical College in the Stephen T.C. Wong lab, I concentrated on drug repositioning for cancers. This focused effort resulted in several high-impact publications, including notable works in Cancer Research (2011, 2013), Drug Discovery Today (2014), and Bioinformatics (2011, 2023). Additionally, my research contributed to a Phase-II clinical trial and the development of one patent, marking significant achievements in advancing cancer therapeutics. Furthermore, my work catalyzed a new research direction within the Wong lab, highlighting the impact and relevance of our collaborative efforts in biomedical research. Currently, I am a tenure-track assistant professor at the Wake Forest School of Medicine focusing on generative AI, foundation models, single-cell data analysis, immunotherapy, and neurodegenerative diseases. My lab published over 40 papers in this field (i10-index=37, h-index=24). My recent scholarly contributions span prestigious journals such as Briefings in Bioinformatics (2021, corresponding author), Cancer Cell (2024, corresponding author), and over 10 additional high-impact publications including The Lancet Oncology (2017), JAMA Oncology (2022), Cancer Cells (2021, 2022), Nature Communications (2018), and Nature Biomedical Engineering (2021), positioning them as a leading authority in machine learning and single-cell studies.
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