Keynote Presentations
Keynote Lecture PASC25 Conference
Fulfilling the Promise of the World’s First Exascale Supercomputer: Science on Frontier
Bronson Messer (Oak Ridge National Laboratory)
The world’s first exascale supercomputer, Frontier, has been in full production for over one year at Oak Ridge National Laboratory. The talk will present some of the features of Frontier’s architecture that make it especially useful for many types of scientific computing, from its pure computing speed to its memory capacity. Some further details of Frontier’s architecture will be discussed, including the new AMD GPUs that provide the bulk of the computational power. Finally, there will be a showcase of some of the most impactful scientific results that have been enabled via the advanced capabilities of Frontier.
Bronson Messer is a Distinguished Staff Scientist and the Director of Science of the Oak Ridge Leadership Computing Facility (OLCF) at the Oak Ridge National Laboratory. He is also a Joint Faculty Professor in the Department of Physics & Astronomy at the University of Tennessee. His primary research interests are related to the explosion mechanisms and phenomenology of supernovae, especially neutrino transport and signatures. He has also worked on machine learning applied to galaxy merger simulations and on performance modeling for HPC architectures. Messer recently served on the American Physical Society’s Committee on Informing the Public (2018-2020) and in 2020 he was awarded the Secretary of Energy’s Honor Award for his part in enabling the COVID-19 High-Performance Computing Consortium.
PASC24 PUBLIC LECTURE
This event is free of charge and open to the general public. The lecture is given in English.
Machine Learning Assessment of Coronary Artery Disease Using X-Ray Angiography
Alberto Figueroa (University of Michigan)
Coronary artery disease (CAD) and coronary microvascular dysfunction (CMD) diagnosis relies on different tests. Percutaneous approaches have made it possible to assess epicardial and microcirculation disease states in a single procedure, although this approach is rarely utilized due to its invasiveness and complexity. Conversely, there are over 4 million cardiac catheterization procedures performed yearly in the USA and Europe alone, making it one of the most used diagnostic procedures, although with a recognized low diagnostic yield.
Given the high number of catheterization procedures and its low diagnostic yield, there is a pressing need to develop methods to extract diagnostic information for both CAD and CMD from coronary angiography data. Our group has developed a data-driven computational models and machine learning tools for anatomical and functional characterization of CAD and CMD using angiography. In this work, we provide an overview of these tools and several applications.
Dr. Figueroa received his PhD in Mechanical Engineering from Stanford University, where he developed fluid-structure interaction methods for blood flow analysis. He was Sr Lecturer in the Division of Biomedical Engineering and Imaging Sciences at King’s College London. Dr. Figueroa is currently the Edward B. Diethrich M.D. Professor of Biomedical Engineering and Vascular Surgery at the University of Michigan.
Dr. Figueroa’s laboratory develops tools for modeling of blood flow which combine imaging, machine learning, and computational methods of fluid and solid mechanics. His group develops the modeling software CRIMSON and is co-founder of AngioInsight, Inc. a company which develops AI tools for assessment of coronary artery disease using x-ray angiography.