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Nicholas Zabaras

Nicholas Zabaras

Viola D. Hank Professor of Computational Science and Engineering

Department of Aerospace and Mechanical Engineering

Viola D. Hank Professor of Computational Science and Engineering
College of Engineering


Phone: 574-631-2429

Office: 311 Cushing Hall


Ph.D., Cornell University, 1987

M.S., University of Rochester, 1983

Diploma, National Technical University (NTU) of Athens, 1982


Nicholas Zabaras joined Notre Dame's College of Engineering after serving as founding director of the Warwick Centre for Predictive Modeling at the University of Warwick and the Hans Fisher Senior Fellow with the Institute for Advanced Study at the Technical University of Munich. Earlier he spent twenty three years serving in all academic ranks of the faculty of the Sibley School of Mechanical and Aerospace Engineering at Cornell University where he was the director of the Materials Process Design and Control Laboratory. He received his PhD in Theoretical and Applied Mechanics at Cornell University, after which he started his academic career at the faculty of the University of Minnesota. He received his Ph.D. in Theoretical and Applied Mechanics from Cornell in 1987, after which he joined the faculty of the University of Minnesota.

Professor Zabaras's research focuses on the integration of computational mathematics, statistics, and science in the predictive modeling of complex systems for scientific and engineering applications. He has been honored with the Wolfson Research Merit Award from the Royal Society, the Michael Tien '72 Excellence in Teaching Prize from Cornell University, and the Presidential Young Investigator Award from the National Science Foundation.


Notre Dame Establishes New Research Center for Delivering Data-driven, Predictive Computational Models

March 8, 2018

In order to better characterize the predictability of computational models and product performance, a new research center at the University of Notre Dame, the Center for Informatics and Computational Science, will develop mathematical, statistical and scientific computing techniques to address the challenges associated with uncertainty quantification.