MS1 Data Mining, Uncertainty Quantification, and Opportunities for Collaboration

Friday, April 27
3:00 PM - 5:05 PM
Room: Pacific Ballroom A - 1st Floo

The fields of data mining and uncertainty quantification have much in common. For example, both deal with issues related to data in high-dimensional spaces, consider statistical aspects of sampling, and build predictive models. In addition, the broad area of data analysis has had to deal with "reasoning under uncertainty", and the term "Uncertainty in AI" has been around a long time. With the recent formation of the two SIAM Activity Groups (SIAGs) on Uncertainty Quantification (UQ) and Data Mining and Analytics (DMA), we see an opportunity for the two communities to come together and discuss topics of mutual interest.

Organizer: Roger Ghanem
University of Southern California, USA
Habib N. Najm
Sandia National Laboratories, USA
Chandrika Kamath
Lawrence Livermore National Laboratory, USA

3:05-3:30
Diffusion on Random Manifolds
Hadi Meidani and Roger Ghanem, University of Southern California, USA

3:35-4:00
Efficient Monte Carlo Computation of Fisher Information Matrix using Prior Information
[PDF, 975KB]
Sonjoy Das, Massachusetts Institute of Technology, USA
James Spall, Johns Hopkins University, USA
Roger Ghanem, University of Southern California, USA

4:05-4:30
Probabilistic Models of Past Climate Change
[PDF, 4MB]
Julien Emile-Geay and Dominique Guillot, University of Southern California, USA
Tapio Schneider, California Institute of Technology, USA
Bala Rajaratnam, Stanford University, USA

4:35-5:00
A Priori Testing of Adaptive Sampling and Sparse PC Representations for Ocean General Circulation Models
[PDF, 3MB]
Justin Winokur, Johns Hopkins University, USA
Patrick R. Conrad, Massachusetts Institute of Technology, USA
Ihab Sraj and Alen Alexanderian, Johns Hopkins University, USA
Mohamed Iskandarani and Ashwanth Srinivasan, University of Miami, USA
Youssef M. Marzouk, Massachusetts Institute of Technology, USA
Omar Knio, Duke University, USA


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