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.
Our research is currently supported by the Pennsylvania Department of Health and NIH through multiple grants (EB022573, AG066650, DK127488, MH120811, and DK117297). We also participate in collaborative research projects supported by NIH grants (MH125829, CA258021, and DA050209).