Iman Javaheri

By |

Year
2020-2021

Research Description
Modeling 3D microstructures using Markov random fields and peridynamics theory

Mentor
Prof. Veera Sundararaghavan, Aerospace Engineering

Eytan Adler

By |

Year
2020-2021

Research Description
High-fidelity multidisciplinary design optimization of air vehicles

Mentor
Prof. Joaquim Martins, Aerospace Engineering

Devina Sanjaya

By |

Graduation Year

2019

Thesis Title

Towards Automated, Metric-Conforming, Mesh Optimization For High-Order, Finite-Element Methods

Current Job

Assistant Professor, Department of Mechanical, Aerospace & Biomedical Engineering, University of Tennessee, Knoxville, TN

Johann Dahm

By |

Graduation Year

2017

Thesis Title

Toward Accurate, Efficient, and Robust Hybridized Discontinuous Galerkin Methods

Current Job

Research Staff Member at IBM

Doreen Fan

By |

Graduation Year

2017

Current Job

Postdoctoral Researcher at the Center for Computational Sciences and Engineering, Berkeley Lab

Jiawen Xie

By |

Graduation Year

2017

Thesis Title

Analytical and Numerical Modeling of Delamination Evolution in Fiber Reinforced Laminated Composites Subject to Flexural Loading

Current Job

R&D Development Manager at Dassault Systèmes

Alex Gorodetsky

By |

Alex Gorodetsky is an Assistant Professor in the Department of Aerospace Engineering. His research includes using applied mathematics and computational science to enhance autonomous decision making under uncertainty. His research has an emphasis on controlling systems that must act in complex environments that are often represented through expensive computational simulations. His research uses tools from wide ranging areas including uncertainty quantification, statistical inference, machine learning, numerical analysis, function approximation, control, and optimization. Several of the key areas he focuses on are: optimal planning by solving large scale Markov decision processes, fast Bayesian estimation for nonlinear dynamical systems, high-dimensional compression and approximation of physical quantities of interest, and fusion of information from varying simulation fidelities and data through multi-fidelity modeling.