Adaptive Decision Making and Improved Data Understanding for Experimental Science Using Statistical Machine Learning and High Performance Computing

Event Sponsor: 
Data Science and Learning Seminar
Start Date: 
Apr 18 2018 - 1:30pm
Building/Room: 
Building 240/Room 4301
Location: 
Argonne National Laboratory
Speaker(s): 
James Ahrens
Speaker(s) Title: 
Los Alamos National Laboratory
Host: 
Ian Foster

Abstract:
Analyzing and extracting scientific knowledge from modern science experiments has become the rate-limiting step in the scientific process. We propose to accelerate  knowledge-discovery from experimental scientific facilities by combining high performance computing and statistical science to produce an adaptive methodology and toolset that will analyze data and augment a scientist's decision-making so that the scientist can optimize experiments in real time. We are developing this capability in the context of dynamic compression experiments, an area of core mission importance and an area that is currently in the midst of substantial increases in the rate of data generation. This project will result in a data science focused information science and technology toolset that is optimized for and will revolutionize dynamic compression science experiments using X-ray user facilities. Furthermore, this work will produce many reusable components that can be applied to multiple scientific domains. When achieved, our approach will allow scientists to elevate their focus above the mundane tasks required for experiment completion to that of making strategic scientific decisions.

Bio:
Dr. James Ahrens is a senior research scientist at the Los Alamos National Laboratory (LANL) and the Data and Visualization lead for the U.S. Exascale Computing Project.Dr. Ahrens graduated in 1996 with a Ph.D. in computer science from the University of Washington. He is the founder and design lead of ParaView, a widely adopted visualization and data analysis package for large-scale scientific simulation data ( http://paraview.org).