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This conference is sponsored by the SIAM Activity Group on Uncertainty Quantification (SIAG/UQ) and is co-sponsored by EPFL and MATHICSE.

epfl    mathicse

This conference is being held in cooperation with the American Statistical Association (ASA) and GAMM Activity Group on Uncertainty Quantification (GAMM AG UQ).

Announcements

All sessions and on-site registration will take place at the SwissTech Convention Center, EPFL Campus:
SwissTech Convention Center
Quartier Nord de l'EPFL
Rte Louis-Favre 2
CH-1024 Ecublens
Lausanne, Switzerland

Registration and all sessions will be located in the SwissTech Convention building.

To RSVP to the conference on Facebook and connect with other attendees, find roommates etc., please visit https://www.facebook.com/events/854857257915533/.

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 #SIAMUQ16.

 

Organizing Committee

Organizing Committee Co-Chairs
Jim Berger, Duke University, USA (ASA Representative)
Fabio Nobile, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (GAMM AG UQ Representative)
Clayton Webster, Oak Ridge National Laboratory, USA

Local Conference Organizers
Antony Davison, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Jan Hesthaven, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Fabio Nobile, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Victor Panaretos, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Organizing Committee
Mihai Anitescu, Argonne National Laboratory, USA
Amy Braverman, Jet Propulsion Laboratory, USA
Albert Cohen, Université Pierre et Marie Curie, France
James (Mac) Hyman, Tulane University, USA
Frances Kuo, University of New South Wales, Australia
Bani K. Mallick, Texas A&M University, USA
Jeremy Oakley, University of Sheffield, United Kingdom
Anthony Patera, Massachusetts Institute of Technology, USA
Clémentine Prieur, Université Joseph Fourier, France
Volker Schulz, University of Trier, Germany
Ralph Smith, North Carolina State University, USA
Andrew Stuart, University of Warwick, United Kingdom
William Welch, University of British Columbia, Canada

 

Description

Uncertainty quantification is critical to achieving validated predictive computations in a wide range of scientific and engineering applications. The field relies on a broad range of mathematics and statistics foundations, with associated algorithmic and computational development. This conference will bring together mathematicians, statisticians, scientists, and engineers with an interest in development and implementation of uncertainty quantification methods.  While applications of UQ in many fields will be represented at the conference, the focal application for UQ16 is Life science.  Other major conference themes include the mathematical foundation of UQ and the connections between UQ and big data.  The goal of the meeting is to provide a forum for the sharing of ideas, and to enhance communication among this diverse group of technical experts, thereby contributing to future advances in the field.

 

Funding Agencies

nsf

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

 

 

 

Themes

Main themes

Topics

 

Important Deadlines

SUBMISSION DEADLINES
September 7, 2015: October 5, 2015: Minisymposium proposals DEADLINE EXTENDED!
October 5, 2015: Contributed Lecture, Poster and Minisymposium Presentation Abstracts

TRAVEL FUND APPLICATION DEADLINE
September 25, 2015: SIAM Student Travel Award and Post-doc/Early Career Travel Award Applications

EARLY BIRD REGISTRATION DEADLINE
January 31, 2016, midnight PST

HOTEL INFORMATION
Visit https://meeting.artegis.com/lw/el/34a11c773784f221d0a18a84b4f98110/Book%20your%20room%20SIAM%20UQ%202016.html?BOOKING_EVENT_ID=11489  for information on hotel proposals and special conference rates.

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