Opening Workshop: August 20 – 24, 2018

Location

This workshop was held at Gross Hall on the campus of Duke University.

Description

The SAMSI program on Model Uncertainty: Mathematical and Statistical brings statisticians and applied mathematicians together with disciplinary scientists from a variety of fields, to better understand the effects of modeling and uncertainty on predictions. This workshop provides the foundation of the MUMS year, examining the theoretical basis for statistical uncertainty, the strengths and weaknesses of models of real world processes and the uncertainty in those processes, computational methods to solve model equations, and the degree of confidence in predictions and inferences resulting from the analysis.

Much of scientific activity of this program will arise from Working Groups – groups of scientists interested in a common theme, who will meet regularly during the year. On Thursday afternoon, a series of brief presentations will be scheduled, to seed the formation of Working Groups. Organizational meetings of these proposed groups will follow, and continue into Friday morning. Among the potential topics of Working Groups are: Foundations of Statistical Model Uncertainty; Modeling Across Scales; Materials Informatics and Mechanics; Reduced Order Models; Uncertainty in Extrapolative Settings; Biomedical Data and Precision Medicine (joint with the PMED program); Stochastic Discretization; Uncertainty in Geoscience; the Small Data problem; Uncertainty and Machine Learning. Multiple Working Groups on similar topics may be formed.

** Planning for this workshop is ongoing. As more information becomes available, it will be updated here **

Tentative Schedule and Supporting Media

Printable Schedule
Speaker Abstracts
Poster Session Titles
Participant List

Confirmed Speakers currently include:

Monday, August 20, 2018
Gross Hall, Duke University, Durham, NC

Description Speaker Slides Videos
Registration
Welcome and Introductory Information
Overview Lectures:
Model Uncertainty and Uncertainty Quantification Merlise Clyde, Duke University
Principles of Predictive Computational Science: Predictive Models of Random Heterogeneous Materials and Tumor Growth Tinsley Oden, University of Texas
An Overview of Reduced-Order Models and Emulators Elaine Spiller, Marquette University
Theoretical Foundations of Model Uncertainty:
Hierarchical Bayesian Models for Inverse Problems and Uncertainty Quantification Bani Mallick, Texas A&M University
On the Impact(s) of Structural Model Error on Simulation Modelling Leonard Smith, London School of Economics, Pembroke College, Oxford
Quantifying Nonparametric Modeling Uncertainty with BART Edward George, Wharton, University of Pennsylvania
Poster Session and Reception

Tuesday, August 21, 2018
Gross Hall, Duke University, Durham, NC

Description Speaker Slides Videos
The Isaac Newton Institute Uncertainty Quantification Programme: A Personal Perspective Peter Challenor, University of Exetor
Panel on Calibration in the Face of Model Discrepancy Matthew Plumlee, Northwestern University
Mengyang Gu, Johns Hopkins
Georgios Karagiannis, University of Durham
Model Reduction:
Machine-Learning Error Models for Quantifying the Epistemic Uncertainty in Low-Fidelity Models Kevin Carlberg, Sandia National Laboratories
Emulators for models and Complexity Reduction Akil Narayan, University of Utah
Data-Driven Discovery of Governing Physical Laws and their Parametric Dependencies in Engineering, Physics and Biology Nathan Kutz, University of Washington

Wednesday, August 22, 2018
Gross Hall, Duke University, Durham, NC

Description Speaker Slides Videos
Extrapolation:
Extrapolation: The Art of Connecting Model-Based Predictions to Reality David Higdon, Virginia Tech
Bound-to-Bound-Data-Collaboration: Prediction on the Feasible Set Michael Frenklach, University of California, Berkeley
Model Discrepancy and Physical Parameters in Calibration and Prediction of Computer Models Jenny Brynjarsdóttir, Case Western Reserve University
Materials:
Modeling and Algorithmic Aspects of UQ for Material with Multiscale Behavior Roger Ghanem, University of Southern California
Materials Innovation Driven by Data and Knowledge Systems Surya Kalidindi, Georgia Institute of Technology
Panel on Materials Laura Swiler, Sandia National Laboratories
Michael Demkowicz, Texas A&M University
Ralph Smith, NC State University
MUMS Workshop Social Event Event sponsored by NC Chapter of ASA

Thursday, August 23, 2018
Gross Hall, Duke University, Durham, NC

Description Speaker Slides Videos
Model and Data Fusion:
UQ Data Fusion: An Introduction and Case Study Robert Wolpert, Duke University
Model Uncertainty in Data Fusion for Remote Sensing Amy Braverman, JPL/Caltech
Inferring Release Characteristics from an Atmospheric Dispersion Model using Bayesian Adaptive Splines Bruno Sanso, University of California, Santa Cruz
Working Groups Overview/Proposals
Working Groups Activity Rooms:  304B, 318, 324, 359, 107

Friday, August 24, 2018
Gross Hall, Duke University, Durham, NC

Description Speaker Slides Videos
Working Group Activity Rooms:  304B, 318, 324, 359, 107
Working Groups Finalized
Shuttle to RDU Airport

Questions: email [email protected]