Extracting knowledge from scientific data—produced from observation, experiment, or computation—presents a significant hurdle for scientific discovery. As U.S. Department of Energy (DOE) has moved toward data-driven scientific discovery, machine learning (ML) has become a critical technology in the modeling of complex phenomena in concert with current computational, experimental, and observational approaches. In the past few years, increased availability of massive data sets and growing computational power have led to breakthroughs in many scientific domains. However, the development of ML systems for many scientific domains poses several challenges such as data paucity, domain-knowledge integration, and adaptability. In this talk, we will present our work on scientific domain-informed ML approaches that seek to overcome these challenges. We will illustrate these methods using case studies on a range of DOE scientific applications. We will conclude with some exciting avenues for future research.
Prasanna Balaprakash is a computer scientist with a joint appointment in the Mathematics and Computer Science Division and the Leadership Computing Facility at Argonne National Laboratory. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. 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. Prior to Argonne, he worked as a Chief Technology Officer at Mentis Sprl, a machine learning startup in Brussels, Belgium. He received his Ph.D. 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.
(c) Department of Computational and Data Sciences, Indian Institute of Science, 2018