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Charbel Farhat

Charbel Farhat

Professor
Professor, Aeronautics and Astronautics
Professor, Mechanical Engineering
Member, Institute for Computational and Mathematical Engineering (ICME)
Diplome d'Ingenieur, Ecole Centrale des Arts et Manufactures, France (1983)
MS, Universite de Paris VI, France, Applied Mechanics (1983)
MS, The University of California, Berkeley, Structural Engineering and Structural Mechanics (1984)
MS, The University of California, Berkeley, Electrical Engineering and Computer Sciences (1986)
PhD, The University of California, Berkeley, Civil Engineering (1987)

Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures at Stanford University, where he is also Chairman of the Department of Aeronautics and Astronautics, Professor of Mechanical Engineering, Professor in the Institute of Computational and Mathematical Engineering, and Director of the King Abdulaziz City for Science and Technology Center of Excellence for Aeronautics and Astronautics. He currently serves on the the Space Technology Industry-Government-University Roundtable. From 2007 to 2018, he served as the Director of the Army High Performance Computing Research Center at Stanford University, and from 2015 to 2019, on the United States Air Force Scientific Advisory Board (SAB). He has also previously served on the technical assessment boards of several national research councils and foundations, and on the United States Bureau of Industry and Security's Emerging Technology and Research Advisory Committee (ETRAC) at the United States Department of Commerce. He was designated by the US Navy recruiters as a Primary Key-Influencer and flew with the Blue Angels during Fleet Week 2014.

He is a Member of the National Academy of Engineering, a Member of the Royal Academy of Engineering (UK), a Member of the Lebanese Academy of Sciences, a Docteur Honoris Causa of Ecole Normale Superieure Paris-Saclay, a Docteur Honoris Causa of Ecole Centrale de Nantes, a designated ISI Highly Cited Author in Engineering, and a Fellow of six professional societies: the Society of Industrial and Applied Mathematics (SIAM), the American Society of Mechanical Engineers (ASME), the International Association of Computational Mechanics (IACM), the World Innovation Foundation (WIF), the United States Association of Computational Mechanics (USACM), and the American Institute of Aeronautics and Astronautics (AIAA). He was knighted by the Prime Minister of France in the Order of Academic Palms and awarded the Medal of Chevalier dans l'Ordre des Palmes Academiques. He is the recipient of several other professional and academic distinctions including the Lifetime Achievement Award from the ASME's Computers & Information in Engineering Division, the Spirit of St Louis Medal from the ASME's Aerospace Division, the AIAA Ashley Award for Aeroelasticity and the Structures, Structural Dynamics and Materials Award from the AIAA, the John von Neumann Medal and the Computational and Applied Sciences Award from the USACM, the Grand Prize from the Japan Society for Computational Engineering and Science (JSCES), the Gauss-Newton Medal, the IACM Award and the Computational Mechanics Award from the IACM, the Gordon Bell Prize and the Sidney Fernbach Award from the Institute of Electrical and Electronics Engineers (IEEE) Computer Society, and the Modeling and Simulation Award from the Department of Defense.

He is also Editor-in-Chief of the International Journal for Numerical Methods in Engineering, Editor of the International Journal for Numerical Methods in Fluids, and a member of the editorial boards of nine other international scientific journals. He has been an AGARD lecturer on aeroelasticity and computational mechanics at several distinguished European institutions, and a plenary or keynote speaker at numerous national and international scientific meetings. He is the author of over 400 refereed journal publications on fluid-structure interaction, computational fluid dynamics on moving grids, computational structural mechanics, computational acoustics, supercomputing, parallel processing, and model order reduction as well as other approaches for physics-based machine learning.