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Professor Nicholas Zabaras


Title

Professor of Uncertainty Quantification

Contact Details

Warwick Centre for Predictive Modelling
University of Warwick
Coventry
CV4 7AL

Room: A408b

Email: nzabaras(at)gmail.com

Phone: +44 (0)24 765 22203
Fax: +44 (0)24 76 418922

Professor Zabaras' Research Webpage

Warwick Centre for Predictive Modelling

Big Data 2015, TUM Institute for Advanced Study, Munich, May 18-20, 2015



About Professor Zabaras

Nicholas Zabaras received his Diploma Degree in Mechanical Engineering at the National Technical University of Athens, Greece (1982), a M.S. in Materials at the University of Rochester, NY (1983) and PhD at Cornell University (1987) in Theoretical and Applied Mechanics. Upon receiving his Ph.D., he joined the faculty of Engineering at the University of Minnesota, Minneapolis, MN. Early research focused on the solution of inverse/design problems in the area of materials processing. In 1991, he returned to Cornell as a faculty of the Sibley School of Mechanical and Aerospace Engineering. At Cornell, he was also member of several other academic fields including Materials Science and Engineering, Applied Mathematics, Computational Science and Engineering. He was the founding director of the Materials Process Design and Control Laboratory (MPDC) that emphasized innovative materials modelling and design research with methodological approaches in mathematics, statistics and scientific computing. Particular areas with major contributions included inverse problems, uncertainty quantification and multiscale/multiphysics modelling.

In the summer of 2014, he joined the University of Warwick as the Chair of Uncertainty Quantification to become the founding director of the Warwick Centre for Predictive Modelling (WCPM). WCPM is a university wide initiative across many colleges and departments that emphasizes integration of computational mathematics, computational statistics and scientific computing to address modelling and design of complex multiscale/multiphysics systems in the presence of uncertainties.

Prof. Zabaras has received several awards for his work. They include a 1991 Presidential Young Investigator Award for his work on Inverse Problems. In 2014, he was appointed as Hans Fisher Senior Fellow at the Institute of Advanced Study at the Technical University of Munich for his work on uncertainty quantification. At the same year he received the Royal Society's Wolfson Research Merit Award. He is Fellow and member of several professional societies. He currently serves as the Associate Editor of the Journal of Computational Physics and as the Editor in Chief of the International Journal for Uncertainty Quantification.

Prof. Zabaras current research is on the integration of computational mathematics, computational statistics and computational science with focus in the predictive modelling of complex systems in scientific and engineering applications. Ph.D or PostDoc applicants and academic/industrial researchers interested for research partnerships or consulting are encouraged to contact him directly.

Research Interests

  • Uncertainty Quantification and Predictive Modelling
  • Bayesian Statistics, Machine Learning, Big Data and Deep Learning
  • High Dimensional Modelling
  • Scientific Computing
  • Stochastic Multiscale and Multiphysics Modeling (Stochastic Coarse Graining, Rare Events, etc.)
  • Inverse Problems and Design in the Presence of Uncertainties
  • Complex Networks and Probabilistic Graphical Models
  • Computational Materials Science, Materials Genome
  • Subsurface Reservoir Engineering

Society Memberships

  • SIAM, Society of Industrial and Applied Mathematics
  • APS, American Physical Society
  • USACM, U. S. Association for Computational Mechanics
  • TMS, The Materials Society
  • ASME, American Association of Mechanical Engineers (Fellow)
  • AAM, American Academy of Mechanics

Recent Publications

  • Ilias Bilionis and N. Zabaras, “A stochastic optimization approach to coarse-graining using a relative-entropy framework”, The Journal of Chemical Physics, Vol. 138, pp. 044313-1 – 044313-12, 2013.
  • Ilias Bilionis and N. Zabaras, “Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification”, Journal of Computational Physics, Vol. 241, pp. 212239, 2013.
  • Peng Chen and N. Zabaras, “A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media”, Journal of Computational Physics, accepted.
  • Jiang Wan and N. Zabaras, “A probabilistic graphical model approach to stochastic multiscale partial differential equations”, Journal of Computational Physics, accepted.
  • Peng Chen and N. Zabaras, “Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification”, Communications in Computational Physics, Vol. 14, No. 4, pp. 851-878, 2013.
  • Bin Wen and N. Zabaras, “A Multiscale Approach for Model Reduction of RandomnMicrostructures”, Computational Materials Science, Vol. 63, pp. 269-285, 2012.
  • Ilias Bilionis and N. Zabaras, “Multidimensional adaptive relevance vector machines for Uncertainty Quantification”, SIAM Journal for Scientific Computing, Vol. 34, No. 6, pp. B881B908, 2012.
  • Ilias Bilionis and N. Zabaras, “Multi-output Local Gaussian Process Regression: Applications to Uncertainty Quantification”, Journal of Computational Physics, Vol. 231, pp. 5718-5746, 2012.
  • Bin Wen and N. Zabaras, “Investigating Variability of Fatigue Indicator Parameters of Two-phase Nickel-based Superalloy Microstructures”, Computational Materials Science, 51 (1), pp. 455-481, 2012.
  • Xiang Ma and N. Zabaras, “Kernel Principal Component Analysis for Stochastic Input Model Reduction”, Journal of Computational Physics, Vol. 230, Issue 19, pp. 7311-7331, 2011.
  • J. Wan and N. Zabaras, “A Bayesian Approach to Multiscale Inverse Problems Using a Sequential Monte Carlo Method”, Inverse Problems, Vol. 27, pp. 105004 (25 pp), 2011.
  • Xiang Ma and N. Zabaras, “A stochastic mixed finite element heterogeneous multiscale method for flow in porous media”, Journal of Computational Physics, Vol. 230, Issue 12, pp. 4696-4722, 2011.
  • Bin Wen, Zheng Li and N. Zabaras, “Thermal response variability of random polycrystalline microstructures”, Communications in Computational Physics, Vol. 10, No. 3, pp. 607-634, 2011.
  • Zheng Li, Bin Wen and N. Zabaras, “Computing mechanical response variability of polycrystalline microstructures through dimensionality reduction techniques”, Computational Materials Science, Vol. 49, Issue 3, pp. 568-581, 2010.
  • B. Kouchmeshky and N. Zabaras, “Microstructure model reduction and uncertainty quantification in multiscale deformation processes”, Computational Materials Science, Vol. 48, Issue 2, pp. 213–227, 2010.
  • X. Ma and N. Zabaras, “High-dimensional stochastic model representation technique for the solution of stochastic PDEs”, Journal of Computational Physics, Vol. 229, no. 10, pp. 3884–3915, 2010.
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