Prof Shen’s research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, medical image analysis, biomedical imaging, and data science. She is particularly interested in developing efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems, including but not limited to, personalized cancer treatment, and precision medicine. Specifically, in the filed of AI/ML, I focus on developing reliable, generalizable, data-efficient machine learning and deep learning algorithms by exploiting prior knowledge from the physical world, such as: Prior-integrated learning for data-efficient ML; Uncertainty awareness for trustworthy ML. In the field of Biomedicine, She focuses on developing efficient computational methods for biomedical imaging and biomedical data analysis to advance precision medicine and personalized treatment, such as: Multi-modal data analysis for decision making; Clinical trial translation for real-world deployment.
Research AreasAI; ML and Statistical Inference
Biology Applications and Engineering
Computational and Informational Processing
Data Processing; Integration; Mining and Visualization