Research Direction
AI for biomedical data, neuroimaging, and recovery prediction.
I develop machine-learning models that connect MRI, EEG, clinical measurements, and behavioral data with neurological disease mechanisms and rehabilitation outcomes.
About Me
I'm a Research Assistant Professor at Peking University. My work focuses on AI and machine learning for biomedical data, especially neuroimaging, Alzheimer's disease diagnosis, stroke recovery prediction, and brain-computer-interface supported rehabilitation. Previously, I was a Postdoctoral Scholar in the Laboratory of NeuroImaging of Prof. Arthur W. Toga at the Keck School of Medicine, University of Southern California, where my research centered on developing and improving AI/ML models for biomedical data to better understand the mechanisms of Alzheimer's disease.
I received my Ph.D. at Tsinghua University in 2024, supervised by Prof. Linhong Ji. During my Ph.D., I was a Visiting Scholar at Harvard Medical School and the Laboratory for Translational Neurorecovery of David Lin, MD, at Massachusetts General Hospital in 2023. My Ph.D. thesis focused on designing and applying artificial intelligence methods to predict and uncover stroke patients' motor recovery based on neurological changes.
I earned my master's degree at George Washington University in 2020, supervised by Prof. Murray Loew at the Medical Imaging & Image Analysis Laboratory. My master's work focused on AI-based medical imaging for tumor segmentation.
Research Focus
Neuroimaging and Clinical AI
Developing multimodal models for MRI, clinical data, and neurological disease diagnosis.
Explainable Biomedical Models
Designing interpretable learning frameworks that map predictions back to clinically meaningful signals.
Stroke Recovery and BCI
Predicting rehabilitation response from EEG, biomechanics, behavioral measures, and corticomuscular coupling.