Research Interests
We provide funded positions for talented Postdoctoral Researcher (PH.D. degree required) and Research Fellow Positions (Master's degree required). If you are interested in the following research topics, please email mh596@duke.edu with your up-to-date CV.
Prof. Huang's Research Lab: https://sites.duke.edu/dukesail/
The Precision Health and Medical Observation Laboratory focuses on transforming the modern healthcare system through the integration of Internet of Things (IoT), machine learning, informatics, and motion and physiological signal sensing. With over 15 years of research expertise, our lab has pioneered innovative, cost-effective, and user-friendly wearable systems that surpass traditional tools. We addressed critical societal and healthcare challenges, including communicable disease monitoring, chronic condition management, early detection of neurodegenerative disorders, and solutions for mobility loss and disability. Central to our mission is the development of digital twin applications for brain science research, leveraging data and computational technologies to enable real-time modeling and simulation of human physiology, clinical workflows, and medical devices. By integrating AI, wearable sensing, large language models, and digital twin platforms, the lab is redefining healthcare from reactive, hospital-based approaches to proactive, evidence-based, and personalized care. Through transdisciplinary collaborations with experts in biomedical engineering, medicine, and nursing, our lab has achieved groundbreaking advances, including IoT sensing innovations, closed-loop AI analytics, and just-in-time patient risk assessments. With over 100 peer-reviewed publications, six patented inventions, and global recognition, we aim to lead the transition to preventative, AI-driven healthcare, enhancing clinical outcomes and improving quality of life worldwide.
Prof. Huang's Research Lab: https://sites.duke.edu/dukesail/
The Precision Health and Medical Observation Laboratory focuses on transforming the modern healthcare system through the integration of Internet of Things (IoT), machine learning, informatics, and motion and physiological signal sensing. With over 15 years of research expertise, our lab has pioneered innovative, cost-effective, and user-friendly wearable systems that surpass traditional tools. We addressed critical societal and healthcare challenges, including communicable disease monitoring, chronic condition management, early detection of neurodegenerative disorders, and solutions for mobility loss and disability. Central to our mission is the development of digital twin applications for brain science research, leveraging data and computational technologies to enable real-time modeling and simulation of human physiology, clinical workflows, and medical devices. By integrating AI, wearable sensing, large language models, and digital twin platforms, the lab is redefining healthcare from reactive, hospital-based approaches to proactive, evidence-based, and personalized care. Through transdisciplinary collaborations with experts in biomedical engineering, medicine, and nursing, our lab has achieved groundbreaking advances, including IoT sensing innovations, closed-loop AI analytics, and just-in-time patient risk assessments. With over 100 peer-reviewed publications, six patented inventions, and global recognition, we aim to lead the transition to preventative, AI-driven healthcare, enhancing clinical outcomes and improving quality of life worldwide.
- Critical Techniques for Personalized and Precise Medicine: Devices, Systems, and Big Data Analytics Platforms (PI: Huang)
- Precision Health and Medicine: Research, Development, and Industrialization (PI: Huang)
- From Digital Factory to Digital Health: Digital Twin Platform Construction for Brain Science Research (PI: Huang)
- Advanced Computing for Precision Health and Medicine Research (PI: Huang)
- Development and Pilot Testing of a Just-in-Time Mobile Smoking Cessation Intervention for Persons Living with HIV (MPI: Huang, Schnall at Columbia University)
- Early Diagnosis of Parkinson's Disease Powered by Data Harnessing, AI, and Transfer Learning (PI: Huang)