Nicholas Zabaras

Short CV

Nicholas J. Zabaras received a diploma in Mechanical Engineering from the National Technical University of Athens in 1982 and a M.Sc in Material Science and Engineering from the University of Rochester in 1983. In 1987, he finished his PhD in Theoretical and Applied Mechanics at Cornell University. After assistant professor positions at the University of Minnesota and Cornell University, he became an associate professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University in 1994, where he was the director of the Materials Process Design and Control Laboratory until 2014. In 1998, he was a visiting professor and CNRS fellow at the École Polytechnique. Since July 2014, he has been the director of the Warwick Centre for Predictive Modelling at the University of Warwick, where he also holds a professorship of Uncertainty Quantification. Nicholas Zabaras is a Fellow of the American Society of Mechanical Engineers, and member of the American Physical Society, the American Academy of Mechanics, the Society for Industrial and Applied Mathematics and the Minerals, Metals & Materials Society.

Awards

2014 Royal Society Wolfson Research Merit Award

2011 Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge

2009 Michael Tien’72 College of Engineering Teaching Award, Cornell University

2006 Fellow, American Society of Mechanical Engineers

1991 Presidential Young Investigator Award

Research Interests

Nicholas Zabaras’ work addresses fundamental problems in the interface of computational mathematics, computational statistics and scientific computing towards predictive modelling of complex multiscale and multiphysics materials systems. Current problems of particular interest include Bayesian uncertainty quantification, modelling of high-dimensional problems, information-theoretic approaches to coarse graining, stochastic model reduction, optimization and design in the presence of uncertainty, modelling of rare events and graph-theoretic approaches to multiscale problems. These themes are important for predictive modelling in materials science (e.g. propagation of uncertainty from ab initio to continuum siumations, modeling of random microstructures and properties, materials genome, etc.) but also relevant to a broad class of multiscale/multiphysics problems in computational science and engineering.

Selected Publications

  • Kristensen, Jesper; Zabaras, Nicholas J.: Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. Computer Physics Communications 185 (11), 2014, 2885-2892 more… BibTeX Full text ( DOI )
  • Chen, Peng; Zabaras, Nicholas: Uncertainty quantification for multiscale disk forging of polycrystal materials using probabilistic graphical model techniques. Computational Materials Science 84, 2014, 278-292 more… BibTeX Full text ( DOI )
  • Wan, J.; Zabaras, N.: Stochastic Input Model generation using Bayesian Network Learning. Journal of Computational Physics 272, 2014, 664-685 more… BibTeX
  • Bilionis, I; Zabaras, N: Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective. Inverse Problems 30 (1), 2013, 015004 more… BibTeX Full text ( DOI )
  • Chen, Peng; Zabaras, Nicholas: A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media. Journal of Computational Physics 250, 2013, 616-643 more… BibTeX Full text ( DOI )
  • Wan, Jiang; Zabaras, Nicholas: A probabilistic graphical model approach to stochastic multiscale partial differential equations. Journal of Computational Physics 250, 2013, 477-510 more… BibTeX Full text ( DOI )
  • Chen, Peng; Zabaras, Nicholas: Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification. Communications in Computational Physics 14 (04), 2013, 851-878 more… BibTeX Full text ( DOI )
  • Kristensen, Jesper; Bilionis, Ilias; Zabaras, Nicholas: Relative entropy as model selection tool in cluster expansions. Phys. Rev. B 87 (17), 2013 more… BibTeX Full text ( DOI )
  • Bilionis, Ilias; Zabaras, Nicholas; Konomi, Bledar A.; Lin, Guang: Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. Journal of Computational Physics 241, 2013, 212-239 more… BibTeX Full text ( DOI )
  • Bilionis, Ilias; Zabaras, Nicholas: A stochastic optimization approach to coarse-graining using a relative-entropy framework. The Journal of Chemical Physics 138 (4), 2013, 044313 more… BibTeX Full text ( DOI )