Our research focuses on characterizing mutational landscape of human gastrointestinal cancer, elucidating the association between mutational footprints and environmental factors, identifying biomarkers with translational significance in immunotherapy and early cancer diagnosis, and interpreting medical imaging data using deep learning algorithms. 

Through bioinformatics analyses, we identified new driver genes underlying the development of gastrointestinal cancer, and shed new lights on genetic markers that are potentially clinically actionable. By applying deep learning algorithm to analyze large-scale sonographic imaging data, we developed a deep learning model that could augment radiologists’ ability in thyroid cancer diagnosis. 

We have published more than 20 peer-reviewed articles in international journals, including Lancet Oncology, JAMA Oncology, and Annals of Oncology etc.