2013: Knowledge Extraction via Comparison of Complex Computational Models to Massive Data Sets: July 29-31, 2013


Advances in computation have significantly improved our abilities to model complex processes in science, engineering and the social sciences. In parallel, experimental observations have grown in size and complexity as well. Gaining knowledge and insight from these efforts requires rigorous comparison of models and data. The ever increasing sophistication of the models along with the size and detail of the heterogeneous data sets demands commensurate advances in the processes and practices of data analysis.

This workshop, co-sponsored by SAMSI and the NSF funded MADAI collaboration (Models and Data Analysis Initiative) was devoted to applying and developing new techniques for the statistical analysis of massively complex models and the application of cutting edge visualization tools to drive data exploration. Currently, MADAI’s analysis infrastructure and work- flows are being designed to address scientific challenges in Heavy-Ion Physics, Cosmology and Climate Sciences. Once fully developed these should be broadly extensible to other domains.

The purpose of the workshop was to introduce a broader base of domain scientists in the aforementioned communities to statistical and visualization tools that facilitate knowledge extraction via complex model to data comparisons. The workshop also provided opportunities for the Statistical Science community to learn about recent developments in complex modeling and computer experiments as well as engage in new collaborative ventures. Two half-day hands-on tutorials showcased a modular visualization platform (based on Paraview) that allows for advanced visualization of complex model dynamics as well as statistical analysis tools. The statistical tools are based on Gaussian process surrogate models for rapid exploration of a model’s parameter space.

Schedule and Supporting Media

Monday, July 29, 2013
at SAMSI, Room 150

Description Speaker Slides Videos
Welcome Scott Pratt, MADAI, MSU
Snehalata Huzurbazar, SAMSI and University of Wyoming
Dynamical modeling of heavy ion collisions – Applying visualization tools and statistical analysis Hannah Petersen, Frankfurt Institute for Advanced Studies  
Exploring the Dark Universe Katrin Heitmann, ANL  
Bayesian Constraints on the Physics of Galaxy Formation Yu Lu, Stanford  
Modeling and Statistical Analysis for Higgs Physics at the Large Hadron Collider Sven Kreiss, NYU  
The Problem of Bias in Projections of Future Climate Charles Jackson, University of Texas  
Statistical Aspects of the Study of ‘Bias’ in Climate Models Gabriel Huerta, University of New Mexico  

Tuesday, July 30, 2013
at SAMSI, Room 150

Description Speaker Slides Videos
Matching Heterogeneous Observations and Computational Models Dorin Drignei, Oakland University  
Bayesian Approaches to the Analysis of Computer Model Output Mark Berliner, Ohio State University  
Some Aspects of Calibration and Exploration of Complex Computer Models Chris Coleman-Smith, Duke University  
Topological Analysis and Visualization in High-Dimensions Timo Bremer, Lawrence Livermore National Laboratory  
The Role of Perception in Visualization and Visual Analytics Christopher Healey, North Carolina State University  
Visualization Designed to Optimize Comprehension: Taking the Viewer into Account Russ Taylor, University of North Carolina  
Discussion of Contributed Stats Questions

Wednesday, July 31, 2013
at SAMSI, Room 150

Description Speaker Slides Videos
MADAI Visualization Tutorial Cory Quammen and Hal Canary, University of North Carolina
MADAI Stats Tutorial Hal Canary, UNC, and Scott Pratt, MSU

Thursday, August 1 and Friday, August 2, 2013
MADAI Members Available at Duke for Collaboration