Workshop II: HPC and Data Science for Scientific Discovery - IPAM

Workshop II: HPC and Data Science for Scientific Discovery

Part of the Long Program Science at Extreme Scales: Where Big Data Meets Large-Scale Computing
October 15 - 19, 2018

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, October 15, 2018

Morning Session

08:00-08:55
Check-In/Light Breakfast (Hosted by IPAM)
08:55-09:00
Welcome and Opening Remarks
09:00-09:50
Karen Willcox (University of Texas at Austin)
Learning low-dimensional models: Nonlinear model reduction via lifting transformations and proper orthogonal decomposition
10:00-10:15
Break
10:15-11:05
Peter Benner (Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg)
Low-rank tensor methods for simulation, optimization and uncertainty quantification of parametric PDEs
11:15-11:30
Break
11:30-12:20
Alexander Szalay (John Hopkins University)
Streaming Algorithms for Halo Finders in Cosmology

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Peter Lindstrom (Lawrence Livermore National Laboratory)
Alternatives to IEEE: NextGen Number Formats for Scientific Computing
15:30-16:00
Break
16:00-16:50
Franck Cappello (Argonne National Laboratory)
Three Frontiers of Lossy Compression for Scientific Data
17:00-18:30
Poster Session & Reception (Hosted by IPAM)

Tuesday, October 16, 2018

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
09:00-09:50
Yannis Kevrekidis (Princeton University)
No equations, no variables, no parameters, no space, no time: Data and the modeling of complex systems
10:00-10:15
Break
10:15-11:05
Begüm Demir (Technische Universität Berlin)
Accurate and Scalable Processing of Big Data in Earth Observation
11:15-11:30
Break
11:30-12:20
Costas Bekas (IBM Zürich Research Laboratory)
Cognitive Discovery: Pushing the Frontiers of Technical R&D with AI.

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Maria Girone (CERN)
Computing Challenges at the Large Hadron Collider (LHC)
15:30-16:00
Break
16:00-16:50
Christoph Koch (Humboldt-Universität)
Retrieving atomic structure from transmission electron microscopy data: a family of inverse problems

Wednesday, October 17, 2018

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
09:00-09:50
Frank Jenko (Max Planck Institute for Plasma Physics/TU Munich)
Combining computing and learning for physics
10:00-10:15
Break
10:15-11:05
Dirk Pflüger (Universität Stuttgart)
Scalability and Fault Tolerance for Exascale Simulations of Hot Fusion Plasmas
11:15-11:30
Break
11:30-12:20
Michael Eldred (Sandia National Laboratories)
Multilevel-Multifidelity Sampling and Emulation for Forward UQ

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Rachel Ward (University of Texas at Austin)
Learning Sparse High-Dimensional Governing Equations from Limited Data
15:30-16:00
Break
16:00-16:50
Clayton Webster (Oak Ridge National Laboratory)
Sparsity-enforced regularizations for optimal learning of high-dimensional systems from random data

Thursday, October 18, 2018

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
09:00-09:50
George Biros (University of Texas at Austin)
Scalable kernel methods
10:00-10:15
Break
10:15-11:05
Omar Ghattas (University of Texas at Austin)
Scalable algorithms for optimal experimental design for large-scale Bayesian inverse problems governed by complex models
11:15-11:30
Break
11:30-12:20
Jochen Garcke (Universität Bonn and Fraunhofer SCAI )
Domain Knowledge in Data Analysis: A Geometrical Method for Low-Dimensional Representations of Simulations

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
James Demmel (University of California, Berkeley (UC Berkeley))
Communication-Avoiding Algorithms for Linear Algebra, Machine Learning, and Beyond
15:30-16:00
Break
16:00-16:50
Adrian Tate (Cray EMEA Research Lab)
Memory- and Data-Centric Abstractions for Management and Optimisation of Data Movement

Friday, October 19, 2018

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
09:00-09:50
Claudia Draxl (Humboldt-Universität)
How FAIR are data repositories in materials science?
10:00-10:15
Break
10:15-11:05
Matthias Scheffler (Fritz-Haber-Institut der Max-Planck-Gesellschaft)
When More Data Do Not Provide A Better Description
11:15-11:30
Break
11:30-12:20
Robert Martin (U.S. Army Research Office)
Efficient Adaptive Hybrid Kinetic Simulation: Computing the Signal in the Noise

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Victoria Stodden (University of Illinois at Urbana-Champaign)
Enabling Reproducibility in Computational and Data-enabled Science
15:30-16:00
Break
16:00-16:50
Mauro Maggioni (Johns Hopkins University)
Learning and Geometry for Stochastic Dynamical Systems in high dimensions
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