Nicholas J. Zabaras

Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987).

His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures.

His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines.

Major Awards and Recognitions:

• Royal Society Wolfson Research Merit Award (2014)
• Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011)
• Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009)
• Fellow, American Society of Mechanical Engineers (2006)
• Presidential Young Investigator Award (1991)

Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.