The first award of the SIAM Activity Group on Computational Science and Engineering Best Paper Prize was presented to Tobin Isaac, Noémi Petra, Georg Stadler, and Omar Ghattas. All four authors have been affiliated with the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. They were awarded the prize at the SIAM Conference on Computational Science and Engineering (CSE19) held February 25-March 1, 2019 in Spokane, Washington. Tobin Isaac gave a talk on the winning paper on February 27, 2019.
Established in 2018, the SIAM Activity Group on Computational Science and Engineering Best Paper Prize is awarded every two years to the authors of the best paper, as determined by the selection committee, on the development and use of mathematical and computational tools and methods for solving problems that may arise in broad areas of science, engineering, technology, and society. The prize recognizes a paper that makes an outstanding and potentially long-lasting contribution to the field. The selection criteria emphasize multidisciplinary work opening up new areas of research and potential broad impact, in addition to novelty, creativity, and overall scientific advancement and quality. The qualifying paper must be published in English, in a peer-reviewed journal with a publication date in the four calendar years preceding the year prior to the award year.
The four authors were recognized for their paper, "Scalable and Efficient Algorithms for the Propagation of Uncertainty from Data through Inference to Prediction for Large-scale Problems, with Application to Flow of the Antarctic Ice Sheet," Journal of Computational Physics (2015). The selection committee deemed this a cornerstone paper in computational science and engineering that demonstrates a scalable algorithmic framework for geophysical model inversion and uncertainty quantification on extreme-scale ice-sheet modeling exploiting supercomputing architectures.
Tobin Isaac is an assistant professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He earned his B.A. in computational and applied mathematics from Rice University in 2007 and his Ph.D. in computational science and engineering from the University of Texas at Austin in 2015. He was awarded the SIAM Activity Group on Supercomputing Early Career Prize
in 2016. He is interested in developing large-scale models of physical systems that can be used to make predictions from data.
Noémi Petra is an assistant professor in the Department of Applied Mathematics in the School of Natural Sciences at the University of California, Merced. She is currently the faculty advisor of the UC Merced SIAM Student Chapter
. Petra earned her B.Sc. in mathematics and computer science from Babeş-Bolyai University, Romania, and earned her Ph.D. in applied mathematics from the University of Maryland, Baltimore County in 2010.
Prior to joining UC Merced, she held an ICES Postdoctoral Fellowship at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Her research interests include large-scale inverse problems governed by differential equations, uncertainty quantification in inference and prediction, and optimal experimental design.
Georg Stadler is a professor of mathematics at New York University's Courant Institute of Mathematical Sciences. Before that, he was a member of University of Texas at Austin's Oden Institute for Computational Engineering and Sciences, initially as a postdoctoral fellow and later as research scientist and lecturer. He received a Ph.D. in mathematics from the University of Graz, Austria in 2004 under the supervision of Karl Kunisch. Stadler's research focuses on computational inverse problems and uncertainty quantification, PDE-constrained optimization and parallel algorithms and solvers. His research is commonly driven by real-world applications such as the simulation and inversion of mantle flows or the dynamics of ice sheets.
Omar Ghattas is the John A. and Katherine G. Jackson Chair in Computational Geosciences, professor of geological sciences and of mechanical engineering, and director of the Center for Computational Geosciences in the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. He is a member of the faculty in the Computational Science, Engineering, and Mathematics (CSEM) interdisciplinary Ph.D. program in the Oden Institute and holds courtesy appointments in the Departments of Computer Science, Biomedical Engineering, and in the Texas Advanced Computing Center. He received his Ph.D. from Duke University in 1988 and held positions on the faculty of Carnegie Mellon University before he joined the faculty of the University of Texas at Austin in 2005. Ghattas has general research interests in forward and inverse modeling, optimization, and uncertainty quantification of large-scale complex mechanical, geological, and biological systems.
The authors collaborated on their answers to the first three questions.
Q: Why are you excited to be awarded the prize?
A: We’re very excited about the prize since it recognizes a paper that brings together the work of several people over multiple years on methods for large-scale forward and inverse problems as well as a challenging application.
Q: Could you tell us a bit about the research that won you the prize?
A: The paper develops parallel methods for Bayesian inference, from satellite observations, of the unobservable basal boundary condition field between the Antarctic ice sheet and the land mass beneath it. This is a step towards improved predictions of future sea level change due to mass loss/gain of land ice. The work required the development of parallel, adaptive, scalable, linear and nonlinear flow solvers capable of handling the anisotropic ice sheet geometry and highly nonlinear ice flow equations; incorporation of satellite surface flow data in an inverse problem governed by these ice flow equations; and approximate Bayesian inference methods to estimate the basal boundary conditions and their uncertainty and propagate them forward to yield predictions of the mass flux into the ocean with associated uncertainty. The algorithms are shown to scale independent of the parameter, state, and data dimensions.
Q: What does your research mean to the public?
A: Our work develops scalable methods for learning large-scale physical models from large-scale data under uncertainty. We applied the methods to the problem of inferring uncertainties in the flow of the Antarctic ice sheet as a demonstration, but they are more broadly applicable to inverse problems that arise across science, engineering, and medicine. Methods similar to ours are currently being adapted and incorporated into climate research production codes.
Q: What does being a SIAM member mean to you?
A: Georg writes: I particularly appreciate SIAM’s international community building and outreach work and its high-quality journal and book publications. I also find it important that SIAM represents the interests of the mathematical and, more generally, scientific community and helps to convey our results to a broader audience.