Trainees will learn how to use DeepHyper for scalable and automated development of deep neural networks. They will also learn about emerging techniques to generalize structured deep neural networks to non-Euclidean domains (geometric deep learning), including: graph neural networks, graph attention networks, message passing networks and graph-to-graph transfer.
Time: February 10, 3-5 p.m. US CT
This session is a part of the ALCF AI for Science Training Series.
About the Speakers
Prasanna Balaprakash is a computer scientist at the Mathematics and Computer Science Division with a joint appointment in the Leadership Computing Facility at Argonne National Laboratory. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focuses on the development of scalable, data-efficient machine learning methods for scientific applications. He is a recipient of U.S. Department of Energy 2018 Early Career Award. He is the machine-learning team lead and data-understanding team co-lead in RAPIDS, the SciDAC Computer Science institute. Prior to Argonne, he worked as a Chief Technology Officer at Mentis Sprl, a machine learning startup in Brussels, Belgium. He received his PhD from CoDE-IRIDIA (AI Lab), Université Libre de Bruxelles, Brussels, Belgium, where he was a recipient of Marie Curie and F.R.S-FNRS Aspirant fellowships.
Minyang Tian is a PhD student at University of Illinois at Urbana-Champaign.