Senior Computer Scientist, Director of the Urban Center for Computation and Data
Charles Catlett is a Senior Computer Scientist at the U.S. Department of Energy’s Argonne National Laboratory and a Senior Fellow at the University of Chicago’s Mansueto Institute for Urban Innovation. His current research focuses on urban data analytics, urban modeling, and the design and use of sensing and “edge” computing technologies embedded in urban infrastructure. He is the principal investigator of the NSF-funded “Array of Things” (AoT), an experimental urban infrastructure to measure the city’s environment with sensors and embedded (“edge”), remotely programmable artificial intelligence hardware. Operating at over 100 locations in Chicago, AoT is expanding to 200 during summer 2019.
Catlett has served as Argonne’s Chief Information Officer and before joining UChicago and Argonne in 2000, he was Chief Technology Officer at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign. From NCSA’s founding in 1985 he participated in the development of NSFNET, one of several early national networks that evolved into what we now experience as the Internet. During the exponential growth of the web following the release of NCSA’s Mosaic web browser, his team developed and supported NCSA’s scalable web server infrastructure. He is the founding director of the Urban Center for Computation and Data at the University of Chicago and is a Computer Engineering graduate of the University of Illinois at Urbana-Champaign.
Director of the Australian National Computational Infrastructure (NCI)
Sean Smith is Director of the Australian National Computational Infrastructure (NCI) and conjointly Professor of computational nanomaterials science and technology at the Australian National University. He has extensive theoretical and computational research experience in chemistry, nanomaterials and nano-bio science and technology. He returned to Australia in 2014 at UNSW Sydney, founding and directing the Integrated Materials Design Centre to drive an integrated program of materials design, discovery and characterization. Prior to this, he directed the US Department of Energy funded Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory, one of five major DOE nanoscience research and user facilities in the US, through its 2011-2013 triennial phase. During his earlier career, he joined The University of Queensland as junior faculty in 1993 after post-doctoral research at UC Berkeley (1991-1993) and Universität Göttingen (Humboldt Fellow 1989-1991); became Professor and Director of the Centre for Computational Molecular Science 2002-2011; and built up the computational nanobio science and technology laboratory the Australian Institute for Bioengineering and Nanotechnology (AIBN) at UQ 2006-2011. He worked with colleagues in the ARC Center of Excellence for Functional Nanomaterials 2002-2011 as Program Leader (Computational Nanoscience) and Deputy Director(Internationalisation).
Professor, Applied Mathematics, Stony Brook University
Yuefan Deng earned his BA (1983) in Physics from Nankai University and his Ph. D. (1989) in Theoretical Physics from Columbia University. He is currently a professor (since 1998) of applied mathematics and the associate director of the Institute of Engineering-Driven Medicine, at Stony Brook University in New York. Prof. Deng’s research covers parallel computing, molecular dynamics, Monte Carlo methods, and biomedical engineering. The latest focus is on the multiscale modeling of platelet activation and aggregation (funded by US NIH) on supercomputers, parallel optimization algorithms, and supercomputer network topologies. He publishes widely in diverse fields of physics, computational mathematics, and biomedical engineering. He has received 13 patents.
Fast and Accurate Multiscale Modeling of Platelets Aided by Machine Learning
Abstract: Multiscale modeling in biomedical engineering is gaining momentum because of progress in supercomputing, applied mathematics, and quantitative biomedical engineering. For example, scientists in various disciplines have been advancing, slowly but steadily, the simulation of blood including its flowing and the physiological properties of such components as red blood cells, white blood cells, and platelets. Platelet activation and aggregation stimulate blood clotting that results in heart attacks and strokes causing nearly 20 million deaths each year. To reduce such deaths, we must discover new drugs. To discover new drugs, we must understand the mechanism of platelet activation and aggregation. To model platelets’ dynamics involves setting the basic space and time discretization in huge ranges of 5-6 orders of magnitudes, resulting from the relevant fundamental interactions at atomic, to molecular, to cell, to fluid scales. To achieve the desired accuracy at the minimal computational costs, we must select the correct physiological parameters in the force fields as well as the spatial and temporal discretization, by machine learning. We demonstrate our results of speeding up a multiscale platelet aggregation simulation, while maintaining desired accuracies, by orders of magnitude, compared with traditional algorithm that uses the smallest of temporal and spatial scales in order to capture the finest details of the dynamics. We present our analyses of the accuracies and efficiencies of the representative modeling. We will also outline the general methodologies of multiscale modeling of cells at atomic resolutions guided by machine learning.
Gilad Shainer is an HPC evangelist that focuses on high-performance computing, high-speed interconnects, leading-edge technologies and performance characterizations. He serves as a board member in the OpenPOWER, CCIX, OpenCAPI and UCF organizations, a member of IBTA and contributor to the PCISIG PCI-X and PCIe specifications. Mr. Shainer holds multiple patents in the field of high-speed networking. He is also a recipient of 2015 R&D100 award for his contribution to the CORE-Direct collective offload technology. Mr. Shainer holds an M.Sc. degree and a B.Sc. degree in Electrical Engineering from the Technion Institute of Technology. He also holds patents in the field of high-speed networking.
Presentation Title: In-Network Computing – The Next Generation of Supercomputing
The National Supercomputing Centre (NSCC) Singapore was established in 2015 and manages Singapore’s first national petascale facility with available high performance computing (HPC) resources to support science and engineering computing needs for academic, research and industry communities.