In this talk, I will describe work my group has carried out in development of deep learning methods that target semantic segmentation and object identification tasks in terapixel Pathology datasets and for satellite data. I will describe what we have been able to achieve, how this work can generalize to additional types of problems, and will outline how exascale computing could be used to transform and integrate our methods and pipelines. I will then go on to outline broad research program in exascale computing and deep learning that promises to identify common deep learning methods for previously disparate large and extreme scale data tasks.
Joel Saltz is Professor of Biomedical Informatics and of Computer Science at Stony Brook University, holds the Cherith Endowed Chair and is Chair of the Department of Biomedical Informatics. He has developed deep learning and machine learning methods that target terapixel Pathology and satellite imaging data and has pioneered active storage and inspector executor runtime complication methods. He has a MD and PhD in Computer Science from Duke University, served as Computer Science Professor at University of Maryland College Park, and launched and chaired Biomedical Informatics departments at Ohio State, Emory, and Stony Brook.