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Focus Area

Structural & Material Systems

Education

PhD, Structural Engineering, The Johns Hopkins University, 2006
MA, Applied Mathematics and Statistics, The Johns Hopkins University, 2006
MS, Structural Engineering, Sharif University of Technology, 2002
BS, Civil Engineering, Sharif University of Technology, 2000

Professional Experience

2021 - Present, Professor, Smead Aerospace Engineering Sciences, ÀÖ²¥´«Ã½
2011 - Present, Affiliated Faculty, Applied Mathematics, ÀÖ²¥´«Ã½
2016 - 2021, Associate Professor, Smead Aerospace Engineering Sciences, ÀÖ²¥´«Ã½
2010 - 2016, Assistant Professor, Aerospace Engineering Sciences, ÀÖ²¥´«Ã½
2009, Visiting Assistant Professor, Aerospace Engineering Sciences, ÀÖ²¥´«Ã½
2007 - 2009, Engineering Research Associate, Center for Turbulence Research, Stanford University
2007 - 2007, Postdoctoral Fellow, Center for Turbulence Research, Stanford University

Awards (selected)

Outstanding Research Award of the Smead Aerospace Engineering Sciences Department, 2024
United Stated Association for Computational Mechanics (USACM) Fellow, 2023 - Present
Felippa-Park Faculty Fellowship, 2023 - Present
Distinguished Performance Award of the Smead Aerospace Engineering Sciences Department, 2022
H. Joseph Smead Faculty Fellow, College of Engineering, CU ÀÖ²¥´«Ã½, 2019-2021
Charles A. Hutchinson Memorial Teaching Award, College of Engineering, 2017
Rome Faculty Fellowship, College of Engineering, CU ÀÖ²¥´«Ã½, 2016-2019
Outstanding Undergraduate Teaching and Mentoring, Department of Aerospace Engineering Sciences, 2016
Dean's Outstanding Teaching Award, 2015
CU ÀÖ²¥´«Ã½ Provost's Faculty Achievement Award, 2015
Outstanding Junior Faculty Award of the Aerospace Engineering Sciences Department, 2015
AIAA-Rocky Mountain Educator of the Year (College) award, 2015
NSF Early Career Research Award, 2015
DOE (ASCR) Early Career Research Award, 2011
Postdoctoral Fellowship, Center for Turbulence Research, Stanford University, 2007

Research Interests

Uncertainty quantification (UQ) of complex systems, scientific machine learning (SciML), data-driven modeling, data compression, randomized algorithms, multi-fidelity modeling, model reduction, design/optimization under uncertainty, statistical inverse modeling, applications in solid and fluid mechanics, as well as in astrodynamics.