Proceedings

• 2019 New York Scientific Data Summit (NYSDS) Conference Proceedings
• 2018 New York Scientific Data Summit (NYSDS) Conference Proceedings
• 2017 New York Scientific Data Summit (NYSDS) Conference Proceedings

NYSDS19 Speakers: Wednesday, June 12, 2019

Keynote:

• Challenges in Climate Science in an Era of Big Data
  Gavin Schmidt (NASA Goddard Institute for Space Studies)

Session 1: Earth and Climate Science (Part 1)

• Mapping Sea-Level Change in Time, Space, and Probability
  Robert Kopp (Rutgers University)
• Pangeo: A Community-Driven Effort for Big Data Geoscience
  Ryan Abernathey (Columbia University-Lamont-Doherty Earth Observatory)
• Improved Forecasts and Understanding of Hurricane Rapid Intensification (Presentation Unavailable)
  Qidong Yang, Chia-Ying Lee, and Michael K. Tippett (Columbia University-Lamont-Doherty Earth Observatory)

Session 2: Earth and Climate Science (Part 2)

• A Multiscale Meta-Modeling Game for Fluid-infiltrating Porous Media
  Kun Wang, WaiChing Sun and Qiang Du (Columbia University)
• Workflow Management for Exascale Global Seismic Tomography
  Jeroen Tromp (Princeton University)
• Converged Simulation and AI Workload Trends in Earth Sciences
  Per Nyberg (Cray Inc.)

Session 3: Computational Astrophysics and Cosmology

• Supernova Astrophysics and Cosmology: The Merger of Simulations and Observations
  Peter Nugent (Lawrence Berkeley Laboratory)
• The First Stars in the Universe
  Greg Bryan (Columbia University-Flatiron Institute)
• The Convergence of Big Data and Large-Scale Simulation: Leveraging the Continuum
  Keynote: David Keyes (King Abdullah University of Science and Technology)

NYSDS19 Speakers: Thursday, June 13, 2019

Keynote:

• Drinking from a Firehose: Solving Data Analysis Challenges Posed by the Anton Supercomputer
  Mark Moraes (D.E. Shaw Research)

Session 4: Streaming Data Analysis

• The ATLAS Experiment at CERN’s LHC: How Do You Analyze 40 Million Physics Pictures per Second?
  Michael Tuts (Columbia University)
• Accelerating Deep Neural Networks for Real-Time Data Selection for High-Resolution Imaging Particle Detectors
  Georgia Karagiorgi (Columbia University)
• AI for Science
  Keynote: Rick Stevens (Argonne National Laboratory-University of Chicago)

Session 5: Biomedical Informatics

• Artificial Intelligence for Near-Real Time Cancer Surveillance: Challenges and Opportunities
  Georgia Tourassi (Oak Ridge National Laboratory)
• Electronic Health Records Based Prediction of Future Incidence of Alzheimer's Disease Using Machine Learning
  Ji Hwan Park, Han Eol Cho, Jong Hun Kim, Melanie Wall, Yaakov Stern, Hynsun Lim, Shinjae Yoo, Hyoung-Seop Kim, and Jiook Cha (Brookhaven National Laboratory, Yonsei University College of Medicine, National Health Insurance Service Ilsan Hospital, and Columbia University)

Session 6: Large-Scale Image Analysis and Mapping

• Picking Particles in Cryo-EM Images Without Knowing Particle Size
  Yuewei Lin, Xiaoning Li, Qun Liu, and Shinjae Yoo (Brookhaven National Laboratory and Stony Brook University)
• Enabling Data-Driven Discovery in Biology: Statistical Learning of Interpretable Mathematical Models from Microscopy Videos
  Suryanarayana Maddu, Bevan Cheeseman, Ivo F. Sbalzarini, and Christian Mueller (TU Dresden, Max Planck Institute, University of Cambridge, and Flatiron Institute)
• A Hierarchical Feature Extraction Pipeline using Resting-state fMRI for Autism Classification
  Qian Wang, Jongwoo Choi, and Xiaofu He (New York State Psychiatric Institute and Columbia University)
• Building the Functional Map of the Fruit Fly Brain
  Aurel A. Lazar (Columbia University)

NYSDS19 Speakers: Friday, June 14, 2019

Session 7: Uncertainty Quantification in Science

• Predictive Data Science for Physical Systems: From Model Reduction to Scientific Machine Learning
  Keynote: Karen Wilcox (University of Texas at Austin)
• Modeling Stochastic Systems using Physics-Informed Deep Generative Models
  Paris Perdikaris and Yibo Yang (University of Pennsylvania)

Session 8: Scalable Algorithms and Computer Systems for Scientific Applications

• Neuromorphic Computing: A Computer Systems Perspective
  Rajit Manohar (Yale University)
• Middleware Building Blocks for Workflow Systems
  Shantenu Jha (Brookhaven National Laboratory/Rutgers University)