Trainees will learn the architecture behind modern classification networks, expanding the power of the problems that can be solved, while increasing the computational complexity required for training.
Corey Adams is an assistant computer scientist at the Argonne Leadership Computing Facility. Originally a high-energy physicist working on neutrino physics problems, he now works on applying deep learning and machine learning techniques to science problems – and still neutrino physics – on high-performance computers. He has experience in classification, segmentation, and sparse convolutional neural networks as well as running machine learning training at scale.
Arvind Ramanathan is a computational biologist in the Data Science and Learning Division at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE). His research interests are at the intersection of data science, high performance computing and biological/biomedical sciences. He has published over 30 papers, and his work has been highlighted in the popular media, including NPR and NBC News. He obtained his Ph.D. in computational biology from Carnegie Mellon University, and was the team lead for integrative systems biology team within the Computational Science, Engineering and Division at Oak Ridge National Laboratory.