The SIAM Workshop on Parameter Space Dimension Reduction will take place at the Omni William Penn Hotel, which is located four blocks from the David L. Lawrence Convention Center (DLCC). SIAM Annual Meeting (AN17), SIAM Conference on Control and Its Applications (CT17), SIAM Conference on Industrial and Applied Geometry (GD17) , and SIAM Workshop on Network Science (NS17) will take place at the DLCC.



Restaurants near Omni William Penn [PDF, 800KB] The Omni is located at numbers 34 and 54.

If you are tweeting about the conference, please use the designated hashtag to enable other attendees to keep up with the Twitter conversation and to allow better archiving of our conference discussions. The hashtag for this meeting is #SIAMDR17. SIAM’s Twitter handle is @TheSIAMNews.


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Organizing Committee

Workshop Co-Chairs
Paul G. Constantine, Colorado School of Mines, USA
David F. Gleich, Purdue University, USA

Organizing Committee
Juan J. Alonso, Stanford University, USA
Nathan Baker, Pacific Northwest National Laboratory, USA
R. Dennis Cook, University of Minnesota, USA
Emilie Dufresne, Oxford University, United Kingdom
Michael S. Eldred, Sandia National Laboratories, USA
Michael Frenklach, University of California Berkeley, USA
Roger Ghanem, University of Southern California, USA
Omar Ghattas, University of Texas at Austin, USA
Mark Girolami, Imperial College London, United Kingdom
Gianluca Iaccarino, Stanford University, USA
Youssef Marzouk, Massachusetts Institute of Technology, USA
Gianluigi Rozza, Scuola Internazionale Superiore di Studi Avanti, Italy
Ralph Smith, North Carolina State University, USA
Michael Wakin, Colorado School of Mines, USA
Rachel Ward, University of Texas at Austin, USA
Brian J. Williams, Los Alamos National Laboratory, USA



Complex computational science and engineering models contain several parameters representing physical inputs. Parameter studies, such as uncertainty quantification or design optimization, need to evaluate many models at different parameter values to endow predictions with credibility and confidence. However, the cost of parameter studies may grow exponentially with the number of inputs. One way to enable parameter studies in highly parameterized models is to identify low-dimensional structures in the map from input parameters to output predictions.

The DR17 workshop brings together researchers across mathematics, statistics, and engineering to explore a range of emerging techniques for parameter space dimension reduction. Topics of interest include:


Funding Agencies

SIAM and the Workshop Organizing Committee wish to extend their thanks and appreciation to the U.S. National Science Foundation for its support of this workshop.





Important Deadlines

February 27, 2017 DEADLINE EXTENDED: MARCH 13, 2017

January 20, 2017 DEADLINE EXTENDED: January 27, 2017 SIAM Student Travel Award and Post-doc/Early Career Travel Award Applications

June 12, 2017: Disconnect time is midnight EDT

June 12, 2017

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