Alireza Doostan
· Professor • Felippa-Park Faculty Fellow Aerospace Mechanics Research Center (AMReC)University of Colorado Boulder · Ann and H.J. Smead Aerospace Engineering Sciences
Active 2004–2026
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About
Alireza Doostan is a Professor in the Ann and H.J. Smead Aerospace Engineering Sciences at the University of Colorado Boulder, where he has been serving since 2021. He holds a PhD in Structural Engineering and a Master's in Applied Mathematics and Statistics from The Johns Hopkins University, as well as degrees in Structural and Civil Engineering from Sharif University of Technology. His research focuses on uncertainty quantification (UQ), computational stochastic mechanics, model reduction for stochastic systems, large-scale statistical inverse analysis, model verification and validation (V&V), estimation theory and data assimilation, and structural dynamics. Throughout his career, he has held various academic and research positions, including assistant and associate professorships at CU Boulder, as well as research roles at Stanford University. Dr. Doostan has received numerous awards for his teaching and research, including the NSF Early Career Research Award, the DOE (ASCR) Early Career Research Award, and the H. Joseph Smead Faculty Fellowship, among others.
Research topics
- Computer Science
- Artificial Intelligence
- Mathematical optimization
- Mathematics
- Machine Learning
- Algorithm
- Applied mathematics
Selected publications
Computer Methods in Applied Mechanics and Engineering · 2021 · 78 citations
Senior authorCorrespondingON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION
International Journal for Uncertainty Quantification · 2020 · 67 citations
Senior authorCorrespondingDue to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often…
Topology optimization under uncertainty using a stochastic gradient-based approach
Structural and Multidisciplinary Optimization · 2020 · 62 citations
Senior authorCorrespondingComputer Methods in Applied Mechanics and Engineering · 2025-08-06 · 3 citations
articleSenior authorCorrespondingSensitivity of Dragonfly Afterbody Radiation to Chemical Kinetic Parameters and Freestream Methane
2025-07-16 · 1 citations
articleDragonfly is a NASA mission that aims to deliver a rotorcraft to explore the near-surface environment of Saturn's moon Titan. The flow around the Dragonfly capsule entering the Titan atmosphere is simulated and used to determine the associated radiation. The goal of the analysis is to determine whether the freestream concentration of methane in the Titan atmosphere can be reliably inferred from backshell radiation instrumentation that will be installed on the Dragonfly capsule. Chemical kinetic…
Recent grants
Frequent coauthors
- 36 shared
Subhayan De
Northern Arizona University
- 30 shared
Stephen Becker
Alpine Quantum Technologies (Austria)
- 27 shared
Kenneth E. Jansen
University of Colorado Boulder
- 26 shared
Gianluca Iaccarino
- 25 shared
Kurt Maute
- 22 shared
John A. Evans
- 20 shared
Jerrad Hampton
International Center for Numerical Methods in Engineering
- 16 shared
Eric Peters
Labs
Education
- 2006
Ph.D., Structural Engineering
The Johns Hopkins University
- 2006
M.A., Applied Mathematics and Statistics
The Johns Hopkins University
- 2002
M.S., Structural Engineering
Sharif University of Technology
- 2000
B.S., Civil Engineering
Sharif University of Technology
Awards & honors
- H. Joseph Smead Faculty Fellow (2018)
- Charles A. Hutchinson Memorial Teaching Award, College of En…
- Rome Faculty Fellowship, College of Engineering, CU Boulder…
- Outstanding Undergraduate Teaching and Mentoring, Department…
- Dean's Outstanding Teaching Award (2015)
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