Lawrence Staib, PhD

Lawrence Staib's research interests are in the development of computational methods for medical image analysis with applications in neuroimaging for the study of normal and abnormal brain function. His work encompasses machine learning approaches to structural and functional imaging as well as multimodal approaches incorporating clinical and other subject information. He has worked on methods for white-matter analysis, functional network analysis, and structural quantification in conditions including autism, depression, and PTSD. He also has a strong interest in the measurement and quantification of uncertainty in machine learning methods.

computational modeling ;  deep learning ;  diffusion imaging ;  functional magnetic resonance imaging ;  neural networks ;  signal processing ;  statistical methods  

algorithms ;  artificial intelligence ;  computational neuroscience ;  computer vision ;  connectivity ;  machine learning ;  biomedical image analysis ;  network analysis  

Staib is a Professor of Radiology and Biomedical Imaging, Biomedical Engineering, and Electrical and Computer Engineering at Yale. He received his AB in Physics from Cornell University. In graduate school at Yale, he studied Computer Science and Electrical Engineering and received his PhD on Bayesian image segmentation. He subsequently joined the Yale faculty, focusing on medical image analysis.