Dr. Yong Fan
Yong Fan, Ph.D., is a tenure track assistant professor of radiology at the University of Pennsylvania. Dr. Fan has a broad background in medical image analysis and pattern recognition, with specific training in applied mathematics, statistics, and machine learning. His research interests are in the field of imaging analytics, machine learning, pattern recognition, and more generally in computational imaging. Much of his work has been focusing on methodology development and applications of machine learning techniques that quantify morphology and function from medical images, integrate multimodal information to aid diagnosis and prediction of clinical outcomes, and guide personalized treatments. The methodological focus has been on the general field of artificial intelligence, with emphasis on machine learning methods applied to complex and large imaging and clinical data. The image analytic methods being and to be developed include functional connectomics, radiomics, image registration and segmentation, and personalized neuromodulatory therapies. On the clinical side, his primary focus is on applications in clinical neuroscience, in cancer, and in chronic kidney disease, aiming to develop precision diagnostic tools using machine learning and pattern recognition techniques. The clinical research studies include brain development, brain diseases such as Alzheimer’s, schizophrenia, depression, and addiction, pediatric kidney diseases, and predictive modeling of treatment outcomes of cancer patients such as rectal and lung cancers.
Dr. Pamela Douglas
Dr. Douglas is a computational neuroscientist whose work lies at the intersection of cognitive neuroscience and artificial intelligence. Dr. Douglas completed a Ph.D. in neuroengineering from UCLA after studying biomedical engineering at Johns Hopkins and the University of Pennsylvania. Current research interests include explainable deep learning, brain computational models, and models of attention.