Opportunities and Challenges for AI-Enhanced Decision Making in Nuclear Proliferation Detection
Ms. Angela M. Sheffield, Eisenhower School, National Defense University, National Nuclear Security Administration, Department of Energy
Presentation | Video Recording

Large-Scale, Data-Driven Methods and Applications Developed as Part of the Exascale Computing Project
Dr. Lori Diachin, Deputy Director, Exascale Computing Project, Lawrence Livermore National Laboratory
Presentation | Video Recording

Applying HPC and AI/ML Capabilities for Stockpile Stewardship Mission
Ms. Thuc Hoang, National Nuclear Security Administration, Department of Energy
Presentation | Video Recording

Decision Support for Wicked Problems
Dr. Robert Lempert, Principal Researcher, RAND Corporation, and Director of the Frederick S. Pardee Center for Longer Range Global Policy and the Future Human Condition
Presentation | Video Recording

Behaviors of People and Systems: Decision Problems and Data Driven Analysis
Dr. Ramayya Krishnan, W. W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Heinz College and the Department of Engineering and Public Policy, Carnegie Mellon University
Presentation

Conflict, Coordination & Control: Do We Understand the Actual Rules Used to Balance Flooding, Energy, and Agricultural Tradeoffs in River Basins?
Dr. Patrick Reed, Joseph C. Ford Professor of Engineering, School of Civil and Environmental Engineering, Cornell University
Presentation | Video Recording

Anomaly Detection for Identifying Fire Hazards in Power Distribution Systems
Dr. Jhi-Young Joo, Distribution Automation Lead Engineer, Lawrence Livermore National Laboratory
Video Recording

Panel Discussion: Decision Making in Complex Systems - Challenges and Opportunities for Applied Math, HPC, and Machine Learning
Moderator: Dr. Francis J. Alexander, Brookhaven National Laboratory
Video Recording

Modeling sea-level rise and its uncertainties under climate change
Dr. Minghua Zhang, Distinguished Professor of Atmospheric Sciences , State University of New York at Stony Brook
Presentation | Video Recording

Data Assimilation and Its Connections with Uncertainty Quantification, Forecast and Machine Learning
Dr. Nan Chen, Assistant Professor, Department of Mathematics, University of Wisconsin-Madison
Presentation | Video Recording

Tropical cyclone risk modeling
Dr. Michael Tippett, Associate Professor, Department of Applied Physics and Applied Mathematics, Columbia University
Presentation | Video Recording

Microbial controls of climate-smart soil health management practices
Dr. Arnab Bhowmik, Assistant Professor of Soil Science and Soil Microbiology, Department of Natural Resources and Environmental Design, College of Agriculture and Environmental Sciences, North Carolina A&T State University
Presentation | Video Recording

From model-based to data-driven remote sensing in environmental management
Dr. Leila Hashemi-Beni, Assistant Professor of Geomatics and the Director of Geospatial Science and Remote Sensing Laboratory, Department of Built Environment at College of Science and Technology, North Carolina A&T State University
Presentation | Video Recording

Planning for Spatially Correlated Failures in Coupled Natural Gas and Power Transmission via Stochastic Optimization
Dr. Seth Blumsack, Professor of Energy Policy and Economics, and Director, Center for Energy Law and Policy, Pennsylvania State University
Video Recording

Panel Discussion: Decision Making in Complex Systems - Challenges and Opportunities for Applied Math, HPC, and Machine Learning
Moderator: Dr. Nathan Urban, Brookhaven National Laboratory
Video Recording

Physics-Informed Deep Learning for Traffic State Estimation and Fundamental Diagram Discovery
Dr. Xuan (Sharon) Di, Associate Professor, Department of Civil Engineering and Engineering Mechanics, Smart Cities Center, Data Science Institute, Columbia University in the City of New York
Presentation | Video Recording

Machine Learning and Game Theory for Societal Challenges
Dr. Fei Fang, Assistant Professor, Institute for Software Research, Carnegie Mellon University
Video Recording

Deep Generative Surrogate Modeling and Inversion in Subsurface Flows
Prof. Nicholas Zabaras, Scientific Computing and Artificial Intelligence (SCAI) Laboratory, University of Notre Dame
Video Recording

Propagation of Uncertainty from Data to Inference for Large-Scale Inverse Problems with Application to Ice Sheet Flow
Dr. Noemi Petra, Associate Professor of Applied Mathematics, School of Natural Sciences, University of California, Merced
Presentation | Video Recording

A probabilistic graphical model foundation to enable predictive digital twins at scale
Dr. Michael Kapteyn, Postdoctoral Fellow, Oden Institute for Computational Engineering and Sciences
Presentation | Video Recording

Tracking and Forecasting Epidemics
Dr. Roni Rosenfeld, Professor and Head, Machine Learning Department, School of Computer Science, Carnegie Mellon University
Video Recording

Panel Discussion: Decision Making in Complex Systems - Challenges and Opportunities for Applied Math, HPC, and Machine Learning
Moderator: Dr. Qiang Du, Columbia University
Video Recording

A Perspective on Autonomous Experimentation and Discovery
Dr. Marcus Noack, Applied Mathematics, Research Scientist, Lawrence Berkeley National Laboratory
Presentation | Video Recording

Co-Design Methodologies for Edge Computing ASICs in Scientific Research Environments
Dr. Sandeep Miryala, Instrumentation Division, Brookhaven National Laboratory
Presentation | Video Recording

ML/AI applications in astronomy
Dr. Anze Slosar, Group Leader for Cosmology & Astrophysics Group, Brookhaven National Laboratory
Video Recording

Nuclear data evaluation with Bayesian networks
Dr. Georg Schnabel, Department of Nuclear Sciences and Applications, International Atomic Energy Agency
Presentation | Video Recording

Automating Particle Accelerator Operations with Machine Learning
Dr. Jonathan Edelen, Senior Research Scientist, Group Leader, RadiaSoft
Presentation | Video Recording

Remote and on-the-fly: artificial intelligence driven science in laboratories and central facilities
Dr. Phillip Maffetone, Assistant Computational Scientist, Data Science and Systems Integration Program, National Synchrotron Light Source II, Brookhaven National Laboratory
Presentation | Video Recording

Panel Discussion: Challenges and Opportunities in Autonomous Scientific Facilities
Moderator: Dr. Kevin Yager, Brookhaven National Laboratory
Video Recording

Full list of posters available from the Agenda

P11: Using Machine Learning for spin-group reclassification of neutron resonances
Presenter: G.P.A. Nobre, National Nuclear Data Center, Brookhaven National Laboratory
Presentation | Video Recording

P13: Deep neural network methods for partial differential equations
Presenter: Jiawei Sun, Ohio State University
Presentation | Video Recording

P16: Applying Bayesian Optimization to Achieve Optimum Cooling at the Low Energy RHIC Electron Cooling System
Presenter: Weijian Lin, Cornell Laboratory for Accelerator Based Sciences and Education, Cornell University
Presentation | Video Recording

P18: Performance Optimization of High-Throughput Virtual Screening Pipelines
Presenter: Hyun-Myung Woo, Department of Electrical and Computer Engineering, Texas A&M University
Presentation | Video Recording

• Keynote: Climate, Carbon, and Water–Tracking and Anticipating Human Impacts
  Dr. Anna Michalak, (Director of the Department of Global Ecology of the Carnegie Institution for Science and Professor in the Department of Earth System Science at Stanford University)
• Quantifying parameter uncertainty within a climate model  | Video Version of Talk
  Oliver Dunbar (California Institute of Technology)
• Numerical Methods for Predicting Coastal Flooding With Uncertainty  | Video Version of Talk
  Kyle Mandli (Columbia University)

• LT1: Artificial Intelligence for the Accuracy and Speed of Multiscale Modeling
• LT2: Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments using Machine Learning
• LT3: Multitask Learning and Multi-Armed Bandit-Based Bayesian Optimization for High-Performance Computing Applications
• LT4: Alzheimer’s Disease Prognosis Using Graph Convolutional Neural Networks
• LT5: Examining Graph Topology using Quantum Walks

• Panel: Challenges and Opportunities for Climate and Environmental Research

• Keynote: Risk in power grids  | Video Version of Talk
  Daniel Bienstock (Columbia University)
• Cyber-Physical Systems for Smart Cities: a Mobility Perspective  | Video Version of Talk
  Desheng Zhang (Rutgers University)
• Computational Modeling at GE  | Video Version of Talk
  Richard Arthur (General Electric Research)

• LT1: Identifying Complex Physics Relationships using Sparse Matrix Decomposition to Inform Plasma Fusion Design
• LT2: Using Unstructured Data to Improve Homelessness and Suicide Prediction
• LT3: e3nn: 3D Euclidean Symmetry Equivariant Neural Networks–Learning from the Geometry and Geometric Tensors of Physical Systems

• Panel: Challenges and Opportunities in Critical Infrastructure and Manufacturing

• Keynote: Re-Engineering the Future of Health with Predictive Models  | Video Version of Talk
  Dr. Grace C.Y. Peng (National Institutes of Health)
• Applications of AI in Cancer Research–Preparations, Progress, and Predictions
  Dr. Eric Stahlberg (Director of Biomedical Informatics and Data Science at Frederick National Laboratory for Cancer Research)
• Data-driven Modeling of COVID-19: Lessons Learned
  Dr. Ellen Kuhl (Robert Bosch Chair of Mechanical Engineering at Stanford University)
• Computational Microscopy of SARS-CoV-2
  Dr. Rommie E. Amaro (Distinguished Professor in Theoretical and Computational Chemistry at the Department of Chemistry and Biochemistry at the University of California, San Diego)

• LT1: The pH-varying Conformational States of SARS-CoV-2 Spike Glycoprotein
• LT2: Millisecond Multiscale Simulations of Multi-Platelet Aggregation in Shear Flow on Supercomputers
• LT3: AI Meets HPC: Learning the Platelet Dynamics from In Vitro and In Silico Experiments

• Panel: Challenges and Opportunities in Computational Medicine/Health

• Keynote: Drug Design and Discovery for SARS-CoV-2 by Integrating Artificial Intelligence and Physics-based Models
  Dr. Arvind Ramanathan (A computational biologist at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering, )
• The Quantum Many-body Problem as a Challenge for Machine Learning Methods  | Video Version of Talk
  Giuseppe Carleo (EPFL, Switzerland)
• The non-uniform FFT and its applications  | Video Version of Talk
  Leslie Greengard (New York University)
• HPC+AI: pushing molecular dynamics simulation with ab initio accuracy to 100 million atoms  | Video Version of Talk
  Lin Lin (University of California - Berkeley)

• LT1: Thermal Analysis of the SARS-CoV-2 Spike Glycoprotein by in silico and in vitro Experiments on the
• LT2: Simulating the Ground State Energies of Molecules Using IBM’s Quantum Emulators
• LT3: Quantum Computations of Dark Energy Models

• Panel: Concluding Discussions–NYSDS 2020

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)

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)

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)