CIUK 2018 Keynote Presentation
26 Nov 2018





Dr Fred Streitz
Director of the HPC Innovation Center
Lawrence Livermore National Laboratory


Fred Streitz is Director of the High Performance Computing Innovation Center (HPCIC) and Chief Computational Scientist at Lawrence Livermore National Laboratory (LLNL). He develops strategies and leads efforts to address the nation’s forefront scientific problems through the application of supercomputing and guides LLNL’s efforts to form strategic industrial, academic and government collaborations that support and expand HPC capability at the Lab.
Fred serves on advisory boards for both Oak Ridge and Argonne National Laboratories and as a Subject Editor for the International Journal of High Performance Computing Applications, in addition to participation in the Advanced Computing Round Table at the Council on Competitiveness. He is a Fellow of the American Physical Society, a two-time winner of the IEEE Gordon Bell Prize, and received a Special Achievement Award from the Secretary of Energy for his efforts in support of the Vice-President’s Cancer Moonshot.
Dr. Streitz received a B.S. in Physics from Harvey Mudd College in Claremont, California and a Ph.D. in Physics from the Johns Hopkins University in Baltimore, Maryland.

Title: Machine Learning and Predictive Simulation: HPC and the U.S. Cancer Moonshot

Abstract: The marriage of experimental science with simulation has been a fruitful one–the fusion of HPC-based simulation and experimentation moves science forward faster than either discipline alone, rapidly testing hypotheses and identifying promising directions for future research. The emergence of machine learning at scale promises to bring a new type of thinking into the mix, incorporating data analytics techniques alongside traditional HPC to accompany experiment. I will discuss the convergence of machine learning, predictive simulation and experiment in the context of one element of the U.S. Cancer Moonshot – a detailed investigation of Ras biology in realistic membranes.

This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Contact: Computing Insight UK