I am currently an Assistant Professor in the Department of Pathology and Laboratory Medicine, and a faculty member of the Federated Learning Research Center at Indiana University School of Medicine, in Indianapolis, IN, USA. My research work so far has focused on developing advanced artificial intelligence-based algorithms to address complex problems in clinical applications, such as the automatic delineation of brain pathologies’ boundaries and the prediction of patient overall survival, by extracting and analyzing computationally extracted imaging/radiomic features, thereby potentially contributing to improving patient management. During the last 3 years of my career, I have been working with the Dr Bakas, and my research has focused on federated learning (FL) for radiologic and histologic imaging as well as algorithmic benchmarking through the organization of challenges. I co-led the largest real-world FL study and the largest computational imaging brain glioblastoma study to-date, involving data of 6,314 brain tumor patients from 71 collaborating sites from all over the globe. I have been leading a multi-site FL effort to accurately detect tumor infiltrating lymphocytes across 12 anatomical areas in histopathology images.
Future:
After joining IU, I am involved in various projects for designing machine/deep learning based computational algorithms for analysis of testicular, breast cancer and brain cancer whole slide images. Considering my prior experience in working with identifying tumor infiltrating lymphocytes using machine learning approach, I am keen in extending this to brain histopathology images.
Post-doctoral Fellowship - University of Pittsburgh, Pittsburgh, PA 2023
Ph.D. - SGGS Institute of Engineering & Technology, Nanded, MH, India 2020
