About Quan Long Quan Long Research Scientist (former), Stochastic Numerics Research Group uncertainty quantification Quan Long worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Quan obtained his Ph.D. degree in computational structural mechanics from CSMLab at Cambridge University, where his research focused on isogeometric analysis using subdivision techniques and its applications in large structural deformations, nematic elastomers, and dynamic fractures. During his postdoctoral research, he extended his interests from deterministic problems to stochastic problems, for example, Bayesian Articles Related News September 2015 Quan has accepted a staff engineer/scientist position at the United Technologies Systems and Controls Engineering, USA 1 min read · Tue, Sep 15 2015 News Dr. Quan Long will join United Technologies Corporation, Connecticut, USA, as an L5 staff engineer starting Fall 2015. Quan started his postdoc in our group in January 2011 and has been promoted to research scientist since October 2013. Quan obtained his Ph.D. at Cambridge University focusing on computational mechanics. August 2015 Dr. Long, Prof. Tempone and colleagues from MIT will organize a mini symposia at the Congress on Industrial and Applied Mathematics (ICIAM 2015) in Beijing, China 1 min read · Thu, Aug 13 2015 News The challenge of optimal information gathering-for the purpose of inference, prediction, design, or control-pervades fields ranging from geophysics to chemical engineering and beyond. These questions can be formalized through the framework of optimal experimental design. Yet extending classical design methodologies to tackle problems of greater scale and dynamic complexity, and to find optimal sequential designs, requires new algorithms and formulations. July 2015 Our research on optimal experimental design is highlighted on KAUST discovery 1 min read · Sun, Jul 12 2015 News A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments. Taking the guesswork out of experimental design 1 min read · Sat, Jul 11 2015 News applied mathematics computational science computing A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments. October 2013 Dr. Quan Long gave an invited talk in Eindhoven University of Technology on Bayesian inverse problems and fast algorithm for experimental design 1 min read · Mon, Oct 14 2013 News Dr. Quan Long has given an invited talk at the Eindhoven University of Technology, Netherlands, on Bayesian inverse problems and fast algorithm for experimental design. The talk was one of the Mechanics, Mathematics and Computation seminars organized by the mechanical engineering department in the university. July 2013 Dr. Quan Long and Profs. Serge Prudhomme and Raul Tempone organized a USNCCM minisymposium on experimental design 1 min read · Mon, Jul 22 2013 News Dr. Quan Long and Profs. Serge Prudhomme and Raul Tempone organized a mini-symposium on Methods and Applications for Experimental Design with Uncertainties. US National Congress of Computational Mechanics, Raleigh, NC, July 22-25, 2013
Quan has accepted a staff engineer/scientist position at the United Technologies Systems and Controls Engineering, USA 1 min read · Tue, Sep 15 2015 News Dr. Quan Long will join United Technologies Corporation, Connecticut, USA, as an L5 staff engineer starting Fall 2015. Quan started his postdoc in our group in January 2011 and has been promoted to research scientist since October 2013. Quan obtained his Ph.D. at Cambridge University focusing on computational mechanics.
Dr. Long, Prof. Tempone and colleagues from MIT will organize a mini symposia at the Congress on Industrial and Applied Mathematics (ICIAM 2015) in Beijing, China 1 min read · Thu, Aug 13 2015 News The challenge of optimal information gathering-for the purpose of inference, prediction, design, or control-pervades fields ranging from geophysics to chemical engineering and beyond. These questions can be formalized through the framework of optimal experimental design. Yet extending classical design methodologies to tackle problems of greater scale and dynamic complexity, and to find optimal sequential designs, requires new algorithms and formulations.
Our research on optimal experimental design is highlighted on KAUST discovery 1 min read · Sun, Jul 12 2015 News A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.
Taking the guesswork out of experimental design 1 min read · Sat, Jul 11 2015 News applied mathematics computational science computing A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.
Dr. Quan Long gave an invited talk in Eindhoven University of Technology on Bayesian inverse problems and fast algorithm for experimental design 1 min read · Mon, Oct 14 2013 News Dr. Quan Long has given an invited talk at the Eindhoven University of Technology, Netherlands, on Bayesian inverse problems and fast algorithm for experimental design. The talk was one of the Mechanics, Mathematics and Computation seminars organized by the mechanical engineering department in the university.
Dr. Quan Long and Profs. Serge Prudhomme and Raul Tempone organized a USNCCM minisymposium on experimental design 1 min read · Mon, Jul 22 2013 News Dr. Quan Long and Profs. Serge Prudhomme and Raul Tempone organized a mini-symposium on Methods and Applications for Experimental Design with Uncertainties. US National Congress of Computational Mechanics, Raleigh, NC, July 22-25, 2013
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