Research Interests
Deep learning and machine learning applications in medical imaging; Lung segmentation
Biography
Nabil Ettehadi is a Ph.D. student and graduate research assistant at the HBIL of the Biomedical Engineering Department at Columbia University. His research interests lie in applying deep learning and machine learning to medical imaging across various modalities, such as CT, MRI, and fMRI. He primarily focuses on lung segmentation in emphysematous tissues using unlabeled or weakly labeled CT scans. Nabil earned his BSc in electrical engineering from Sharif University of Technology in Tehran, Iran, in 2015, and his MS in electrical engineering from the University of Central Florida (UCF) in 2018. During his time at UCF, he worked on machine learning applications in assistive robotics and autonomous agents for helping individuals with disabilities. He began his Ph.D. in biomedical engineering at Columbia University in August 2018, concentrating on medical imaging.