8:00 - 8:55

Check-In/Light 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)

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

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

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)

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