Dr. Li leads research at the intersection of artificial intelligence, physics, medical imaging, and oncology. His work focuses on deep learning for biomedical image analysis, with core expertise in generative modeling, uncertainty modeling, and multi-modal representation learning.
His ongoing projects at NUS Medicine and BME center on machine learning to improve the diagnostic power of PET/CT and PET/MRI scans in radiology and oncology. He also collaborates with physicists on novel imaging techniques, such as low-field MRI and synchrotron X-ray imaging, to enable fast acquisition and high-throughput image analysis.
He serves as a senior program committee member for AAAI (2026), ECAI (2025), and an area chair for MICCAI (2024, 2025). At MICCAI, He organized benchmarks on medical image segmentation (e.g., FeTA) and synthesis (e.g., BraSyn).