Biomedical image analysis and machine learning
My laboratory aims to develop advanced image analysis and machine learning strategies to identify structural and functional (fMRI-based) image-derived biomarkers of neurological and developmental disorders, focusing on Autism Spectrum Disorders (ASD). These biomarkers are useful for diagnosing and predicting response outcomes from several therapies. I have broad expertise in medical image analysis and medical image computing, including developing algorithms specifically aimed at task-based and resting-state-based functional MR image data. Current work includes the development of deep learning approaches for functional connectivity and time series analysis and their use in a variety of disease classification/biomarker identification and regression/ outcome prediction tasks.
Jim Duncan received his BSEE with honors from Lafayette College, his MS from UCLA, and PhD in Electrical Engineering from the University of Southern California. He has been on the faculty at Yale since 1983, the Ebenezer K. Hunt Professor of Biomedical Engineering, Radiology & Biomedical Imaging since 2007, and the Chair of the Department of Biomedical Engineering since 2022. He also holds secondary appointments in Electrical Engineering and Statistics & Data Science. In addition, Jim enjoys running, skiing, golfing, and spending time with his wife, Kathy, son Scott, daughter-in-law Caroline, and daughters Allison and Kirsten.
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE resultsMedical Image Analysis (2020)
Medical Image Analysis (2021)
Machine Learning in Medical Imaging (2017)