Workshop I: Big Data Meets Large-Scale Computing
September 24 - 28, 2018
Monday, September 24, 2018 | |
8:00 - 8:55 | Check-In/Breakfast (Hosted by IPAM) |
8:55 - 9:00 | Welcome and Opening Remarks |
9:00 - 9:50 | 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 |
12:30 - 2:30 | Lunch (on your own) |
2:30 - 3:20 | Alexander Szalay (John Hopkins University) Numerical Laboratories: the Road to Exascale |
3:30 - 4:30 | Break |
4:30 - 5:30 | Emmanuel Candes (Stanford University) Public Lecture - Green Family Lecture Series: “Sailing Through Data: Discoveries and Mirages” |
5:30 - 6:45 | Poster Session & Reception (Hosted by IPAM) |
Tuesday, September 25, 2018 | |
8:00 - 9:00 | Continental Breakfast |
9:00 - 9:50 | 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 |
12:30 - 2:30 | Lunch (on your own) |
2:30 - 3:20 | Chris Johnson (University of Utah) Big Data Meets Large-Scale Visualization |
3:30 - 4:00 | Break |
4:00 - 4:50 | Valerio Pascucci (University of Utah) Extreme Data Management Analysis and Visualization for Exascale Supercomputers and Experimental Facilities |
Wednesday, September 26, 2018 | |
8:00 - 9:00 | Continental Breakfast |
9:00 - 9:50 | 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) 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 |
12:30 - 2:30 | Lunch (on your own) |
2:30 - 3:20 | Sherry Li (Lawrence Berkeley National Laboratory) A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression |
3:30 - 4:00 | Break |
4:00 - 4:50 | Per-Gunnar Martinsson (University of Colorado Boulder) Randomized projection methods for reducing communication in matrix computations |
Thursday, September 27, 2018 | |
8:00 - 9:00 | Continental Breakfast |
9:00 - 9:50 | 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 |
12:30 - 2:00 | Lunch (on your own) |
2:00 - 2:50 | Ion Stoica (University of California, Berkeley (UC Berkeley)) Ray: A System for Distributed AI |
3:00 - 4:00 | Discussion |
4:30 - 5:30 | Emmanuel Candes (Stanford University) Public Lecture - Green Family Lecture Series: “The Knockoffs Framework: New Statistical Tools for Replicable Selections” |
5:30 - 6:45 | Reception (Location: IPAM Lobby) |
Friday, September 28, 2018 | |
8:00 - 9:00 | Continental Breakfast |
9:00 - 9:50 | 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 |
12:30 | Conclusion |