About
Yiren Shen is a postdoctoral researcher with the NSF AI Institute in Dynamic Systems at the University of Washington, where he works on AI-enabled design automation, physics-informed machine learning, and multi-fidelity optimization for sustainable aircraft and energy systems. He completed his Ph.D. in Aeronautics and Astronautics at Stanford University under Professor Juan J. Alonso, and he holds an M.S. in Aeronautics and Astronautics from Stanford and a B.S. in Mechanical Engineering from Cornell University.
His research sits at the intersection of aerospace design, surrogate modeling, and scientific machine learning. He develops graph neural networks and other data-driven methods to bridge low-fidelity and high-fidelity analyses, accelerate design-space exploration, and improve decision-making in conceptual aircraft design. He also contributes to open-source tools and multi-fidelity modeling workflows used in aerodynamic analysis and optimization.
Research
- Physics-informed and data-driven surrogate modeling
- Multi-fidelity aerodynamic analysis and design space exploration
- AI-enabled optimization for conceptual aircraft design
- Uncertainty-aware design tools and scientific machine learning
Education
- Ph.D., Aeronautics and Astronautics, Stanford University, 2022-2026
- M.S., Aeronautics and Astronautics, Stanford University, 2017-2019 (conferred 2024)
- B.S., Mechanical Engineering, Cornell University, 2014-2017