معرفی
Alireza Doostan is a Professor in the Smead Aerospace Engineering Sciences department at the University of Colorado Boulder. He is also an Affiliated Faculty member in Applied Mathematics at the same institution. His research focuses on uncertainty quantification, computational stochastic mechanics, and scientific machine learning, with applications to aerospace and mechanical systems.
- 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)
His work emphasizes uncertainty quantification (UQ), model reduction for stochastic systems, and data assimilation in mechanics. He develops methods for statistical inverse analysis, design under uncertainty, and data-driven model discovery applied to fluid and structural dynamics.
Recent publications focus on scientific machine learning (e.g., PINNs for Li-ion batteries), bi-fidelity methods for data reduction, and stochastic optimization in aerospace design. These works span topics like turbulence modeling, spacecraft trajectory planning, and adaptive data compression for PDE systems.
Selected scientific awards include the H. Joseph Smead Faculty Fellow (2018), NSF and DOE Early Career Awards (2015, 2011), and multiple teaching accolades. He leads the UQ and Data-Driven Lab within the Aerospace Mechanics Research Center (AMREC).
