Stanford University
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Mani Ardalan Farhadi
Facilities Senior Planner, School of Medicine - Office of Facilities Planning & Management
Current Role at StanfordAs Senior Facilities Planner in the OFPM (Office of Facilities, Planning and Management) for the SoM (School of Medicine), I collaborate with Dept. Chairs, Faculty, Researchers, Staff and Facilities to optimize layouts for appx. 2M square feet of space as adjustments are needed to offices, labs, and teaching spaces across the campuses of Stanford University. I work with consultants, designers, project managers, furniture vendors, movers and others to determine the optimal solutions. After 30+ years of being in architectural firms working with educational institutions, I'm thrilled to be inside that setting and contributing to the success of the School of Medicine.
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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
On Partial Leave from 04/01/2026 To 06/30/2026Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design, analysis, and digital twinning of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on reliable autonomous carrier landing in rough seas; dissipation of vertical landing energies through structural flexibility; nonlinear aeroelasticity of N+3 aircraft with High Aspect Ratio (HAR) wings; pulsation and flutter of a parachute; pendulum motion in main parachute clusters; coupled fluid-structure interaction (FSI) in supersonic inflatable aerodynamic decelerators for Mars landing; flight dynamics of hypersonic systems and their trajectories; and advanced digital twinning. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-component, multi-physics problems; discrete-event-free embedded boundary methods for CFD and FSI; efficient Bayesian optimization using physics-based surrogate models; modeling and quantifying model-form uncertainty; probabilistic, physics-based machine learning; mechanics-informed artificial neural networks for data-driven constitutive modeling; and efficient nonlinear projection-based model order reduction for time-critical applications such as design, active control, and digital twinning.
