Prof. Lizhou FAN

Dr. Lizhou Fan is a Vice-Chancellor Assistant Professor at The Chinese University of Hong Kong (CUHK), Department of Psychiatry. Previously, he was a postdoctoral research fellow at the Harvard–Mass General Brigham AI in Medicine Program. Before that, he received a PhD in Information from the University of Michigan School of Information (UMSI) and a BS in Statistics from the University of California, Los Angeles (UCLA).

As a member of the Department of Psychiatry in the Faculty of Medicine at CUHK, his research is dedicated to advancing Medical Artificial Intelligence and Health Informatics, with a focus on Digital Mental Health and AI-driven Psychiatry. He is committed to fostering interdisciplinary collaborations between the AI community and medical and healthcare professionals. To better facilitate mutual understanding, his primary research interests can be outlined from two complementary perspectives.

From the AI perspective, his research interests include:

Clinical Reasoning with Large AI Models. With the rapid advancement of intelligent models—including deep learning, large language models (LLMs), multimodal foundation models, and energy-based models—there is significant potential to enhance complex clinical decision-making processes. His research aims to develop clinically applicable AI models, with a particular emphasis on Psychiatry, addressing the current gap in practical applications.

System Building with AI Agents. He is interested in developing sophisticated AI-driven systems capable of simulating realistic patient interactions and facilitating clinical training and assessment. This work includes the design and implementation of interactive AI platforms that can reliably support medical professionals and improve patient care, particularly in Digital and Computational Psychiatry.

From the medical and healthcare perspective, his research focuses on neurodegenerative, neurocognitive, and neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD) and Parkinson’s Disease (PD). He is also interested in digital and AI-driven methods for self-harm prevention (e.g., NSSI) and sleep disorders analysis (e.g., iRBD). Specific applications of his research include:

Auxiliary Diagnostics and Mechanism Understanding. By leveraging AI and multimodal data integration, he seeks to enhance early and precise diagnosis of neurological disorders and to unravel underlying disease mechanisms through computational modeling and data-driven insights.

Digital Intervention and Patient-Facing Applications. He develops innovative digital tools aimed at personalized intervention, continuous monitoring, and improved patient engagement, with the goal of empowering patients and caregivers through accessible, AI-powered technologies.