Workshop I: Big Data Meets Large-Scale Computing - IPAM

Workshop I: Big Data Meets Large-Scale Computing

Part of the Long Program Science at Extreme Scales: Where Big Data Meets Large-Scale Computing
September 24 - 28, 2018

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, September 24, 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
David Keyes (King Abdullah Univ. of Science and Technology (KAUST))
The Convergence of Big Data and Extreme Simulation
10:00-10:15
Break
10:15-11:05
Benjamin Peherstorfer (Courant Institute of Mathematical Sciences)
Data-Driven Multifidelity Methods for Monte Carlo Estimation and Beyond
11:15-11:30
Break
11:30-12:20
Gael Varoquaux (Institut National de Recherche en Informatique et Automatique (INRIA))
Detecting psychiatric disorders with statistical learning tailored to brain activity

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Alexander Szalay (John Hopkins University)
Numerical Laboratories: the Road to Exascale
15:30-16:30
Break
16:30-17:30
Emmanuel Candes (Stanford University)
Public Lecture - Green Family Lecture Series: “Sailing Through Data: Discoveries and Mirages”
17:30-18:45
Poster Session & Reception (Hosted by IPAM)

Tuesday, September 25, 2018

Morning Session

08:00-09:00
Continental Breakfast
09:00-09:50
Hans-Joachim Bungartz (Technical University Munich (TUM))
Sparse grids and their impact on HPC and Big Data
10:00-10:15
Break
10:15-11:05
Michael Griebel (University of Bonn)
Manifold learning by sparse grid methods
11:15-11:30
Break
11:30-12:20
Dirk Pflüger (Universität Stuttgart)
Numerical data mining with sparse grids at extreme scale

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Chris Johnson (University of Utah)
Big Data Meets Large-Scale Visualization
15:30-16:00
Break
16:00-16:50
Valerio Pascucci (University of Utah)
Extreme Data Management Analysis and Visualization for Exascale Supercomputers and Experimental Facilities

Wednesday, September 26, 2018

Morning Session

08:00-09:00
Continental Breakfast
09:00-09:50
Moses Charikar (Stanford University)
Importance Sampling in High Dimensions via Hashing
10:00-10:15
Break
10:15-11:05
Marina Meila (University of Washington – Tacoma)
Non-linear dimension reduction in the age of big data
11:15-11:30
Break
11:30-12:20
Emmanuel Candes (Stanford University)
Is non-convex optimization really hard? A couple of recent stories

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-15:20
Sherry Li (Lawrence Berkeley National Laboratory)
A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
15:30-16:00
Break
16:00-16:50
Per-Gunnar Martinsson (University of Colorado Boulder)
Randomized projection methods for reducing communication in matrix computations

Thursday, September 27, 2018

Morning Session

08:00-09:00
Continental Breakfast
09:00-09:50
Asch Mark (Université de Picardie (Jules Verne))
Model Inversion and Data Assimilation for Decision-Making in an Uncertain but Data-Rich World
10:00-10:15
Break
10:15-11:05
Omar Ghattas (University of Texas at Austin)
Scalable algorithms for optimal training data for Bayesian inference of large scale models
11:15-11:30
Break
11:30-12:20
Carlos Andrade Costa (IBM Thomas J. Watson Research Center)
Converged Ecosystem for Data Analytics and Extreme-Scale Computing

Afternoon Session

12:30-14:00
Lunch (on your own)
14:00-14:50
Ion Stoica (University of California, Berkeley (UC Berkeley))
Ray: A System for Distributed AI
15:00-16:00
Discussion
16:30-17:30
Emmanuel Candes (Stanford University)
Public Lecture - Green Family Lecture Series: “The Knockoffs Framework: New Statistical Tools for Replicable Selections”
17:30-18:45
Reception (Location: IPAM Lobby)

Friday, September 28, 2018

Morning Session

08:00-09:00
Continental Breakfast
09:00-09:50
Marc Genton (King Abdullah Univ. of Science and Technology (KAUST))
A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes
10:00-10:15
Break
10:15-11:05
Rio Yokota (Tokyo Institute of Technology)
Optimization Methods for Large Scale Distributed Deep Learning
11:15-11:30
Break
11:30-12:20
Paris Perdikaris (University of Pennsylvania)
Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling and computation under uncertainty

Afternoon Session

12:30-12:30
Conclusion
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