School of Engineering
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Haoquan Fang
Ph.D. Student in Computer Science, admitted Autumn 2026
BioHaoquan is an incoming CS PhD student at Stanford University, advised by Prof Fei-Fei Li as part of the Stanford Vision and Learning Lab. Currently, He is also a research intern at NVIDIA Cosmos Lab working with VP Ming-Yu Liu and Dr Haotian Zhang.
Previously, he has spent time at Ai2 PRIOR and Ai2 Robotics. He obtained my BS degree from the University of Washington, where he double majored in computer science (with honors) and statistics, and minored in mathematics. He was advised by Prof Ranjay Krishna, Prof Ali Farhadi, Prof Dieter Fox, and Prof Jenq-Neng Hwang. He was also mentored by and collaborate with Prof Jiafei Duan and Dr Ying Jin.
His research interests lie broadly in robot learning. In particular, he focuses on developing foundation models for robotic manipulation that are deployable in the real world and unlock novel capabilities. -
Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
Current 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.
