Machine Learning for Biomedical Data Analysis

We are a research lab working on machine learning for biomedical imaging data analysis. Our research focuses on the field of imaging analytics, machine learning, pattern recognition, and more generally in computational imaging. We have been focusing on both 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, our 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.

We are looking for new members

Multiple postdoctoral positions in medical image analysis and machine learning are available at the Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. Candidates must have a background in Image Processing, Statistical Analysis, or Pattern Recognition. Experience in fMRI data analysis, image segmentation, image registration, or deep learning is a plus. The postdoctoral fellows will have opportunities to work on deep learning for image segmentation, image registration, fMRI data analysis, and image based predictive modeling.
Please e-mail CV to Dr. Fan.