Past Event

New York Scientific Data Summit 2019

June 12, 2019 - June 14, 2019
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Schapiro CEPSR, 530 W. 120 St., New York, NY 10027 Davis Auditorium
The 2019 New York Scientific Data Summit (NYSDS) is the fifth in a symposia series established by Brookhaven National Laboratory (BNL), and led by its Computational Science Initiative, which aims to accelerate data-driven discovery and innovation in science and industry. It brings together researchers, developers and end-users from academia, industry, utilities, and state and federal governments. The summit is a forum to connect diverse participants, from the greater New York region, as well as nationally and internationally, and to foster discussion and collaboration. This year, the summit will be hosted and co-organized by Columbia University's Computing Systems for Data-Driven Science center, which is part of its Data Science Institute (DSI). This center is a nexus at Columbia for research in large-scale computer systems design, data analytics, and applications to cutting-edge problems in science, engineering and medicine. With keynote speakers from industry and international big-science projects, as well as a range of regular talks (both invited and submitted), the 2-1/2 day symposium is organized into six topic areas Streaming Data Analysis and Large-Scale Simulation Scalable Algorithms and Computer Systems for Scientific Applications Large-Scale Image Analysis and Mapping Focus Topic #1: Biomedical Informatics Focus Topic #2: Earth and Climate Science Focus Topic #3: Computational Astrophysics and Cosmology Keynote Speakers David Keyes (KAUST) - The convergence of big data and large-scale simulation: leveraging the simulation-data-edge continuum for science Mark Moraes (DE Shaw Research, NY, NY) - Drinking from a firehose: solving data analysis challenges posed by the Anton supercomputer Gavin Schmidt (NASA Goddard Institute for Space Studies, NY, NY) - Challenges in climate science in an era of big data Rick Stevens (Argonne National Laboratory/University of Chicago) - AI for science Karen Willcox (UT Austin) - Projection-based model reduction: formulations for physics-based machine learning For complete, updated information and to register, please visit To read about the conference on the Earth Institute blog, visit

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