In this video from the Argonne Training Program on Extreme-Scale Computing, Prasanna Balaprakash from Argonne presents: Overview of Machine Learning Methods.
Machine learning enables systems to learn automatically, based on patterns in data, and make better searches, decisions, or predictions. Machine learning has become increasingly important to scientific discovery. Indeed, the U.S. Department of Energy has stated that “machine learning has the potential to transform Office of Science research best practices in an age where extreme complexity and data overwhelm human cognitive and perception ability by enabling system autonomy to self-manage, heal and find patterns and provide tools for the discovery of new scientific insights.
Prasanna Balaprakash‘s research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focus is on the automated design and development of scalable algorithms for solving large-scale problems that arise in scientific data analysis and in automating application performance modeling and tuning. He received a bachelor’s degree in computer science engineering from the Periyar University in Salem, India; a master’s degree in computer science from the Otto-von-Guericke University Magdeburg in Germany; and a Ph.D. in engineering sciences from CoDE-IRIDIA (AI Lab), Université libre de Bruxelles, Brussels, Belgium, where he was a Marie Curie fellow and later an FNRS Aspirant. He was the chief technology officer at Mentis Sprl., a data analytics startup in Brussels, Belgium for a year before moving to Argonne in late 2010, where he was a postdoc until late 2013.