CV

Summary

Yiren Shen is a postdoctoral researcher with the NSF AI Institute in Dynamic Systems at the University of Washington, working at the intersection of aerospace design, physics-informed machine learning, surrogate modeling, and multi-fidelity optimization. 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 work focuses on AI-enabled design automation, data-driven aerodynamic surrogate modeling, uncertainty-aware optimization, and sustainable unconventional vehicle development.

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• Download Yiren Shen CV (October 2026)

Education

• Ph.D. in Aeronautics and Astronautics, Stanford University, 2022-2026
• M.S. in Aeronautics and Astronautics, Stanford University, 2017-2019 (conferred 2024)
• B.S. in Mechanical Engineering, Cornell University, 2014-2017
• Freshman Coursework in Aerospace Engineering, University of Illinois Urbana-Champaign, 2013-2014

Work experience

• Oct. 2026 – present: University of Washington, NSF AI Institute in Dynamic Systems
  • Postdoctoral Researcher
  • Developing state-of-the-art benchmarks for coupled aerodynamic, structural, and control optimization.
• Jul. 2026 – Sep. 2026: Stanford University, Aerospace Design Lab
  • Postdoctoral Researcher
  • Metroplex Overflight Noise Analysis (MONA) departure noise analyst.
• Jun. 2025 – Sep. 2025: GE Aerospace Research, Probabilistic Design and Material Informatics
  • Research Fellow
  • Proposed and developed a large-eddy simulation flow super-resolution program for improving low-order LES fields to high-order equivalents with credible integrated quantities of interest.
  • Surveyed state-of-the-art physics-informed machine learning methods and integrated selected approaches into GE property software workflows.
• Jun. 2023 – Sep. 2023: SRI International, Intelligent Systems Lab
  • Research Intern
  • Composed a finite element analysis program for structural simulation using MITC4 shell elements.
  • Integrated the FEA code with topology optimization heuristics for reinforcement fiber layout optimization applications.
• Jan. 2022 – Jun. 2026: Stanford University, Aerospace Design Lab
  • Doctoral Researcher
  • Advisor: Prof. Juan J. Alonso
  • Research focuses on physics-informed machine learning, applied surrogate modeling, AI-driven design space exploration, automated design tools, and multi-fidelity analysis methods for sustainable unconventional vehicle development.
  • Developed VortexNet, a graph neural network-based multi-fidelity surrogate model that augments low-fidelity aerodynamic tools with high-fidelity CFD field data.
  • Established empirical scaling laws for GNN-based aerodynamic field surrogates and released a multi-fidelity aerodynamic dataset.
  • Demonstrated the workflow in a stability-constrained aerodynamic shape optimization problem.
  • Contributor to the SCALOS project for supersonic configurations at low speeds, with emphasis on surrogate modeling, SST design space exploration, and planform optimization.
• Aug. 2020 – Oct. 2021: Zhejiang University, Laboratory of Flexible Sensors and Intelligent Interaction
  • Visiting Scholar
  • Performed conformal mapping simulation for hyper-elastic soft sensor deployment on complex robot arm surfaces.
  • Proposed, designed, and simulated a soft hair-like flow sensor for small drone airspeed sensing.
• Jun. 2018 – Sep. 2018: Honda Aircraft Company, Department of Advanced Research
  • R&D Engineer
  • Evaluated a stability augmentation system and determined voting thresholds for low false-alarm rates.
  • Improved data extraction and analysis efficiency by introducing a plotting framework.
  • Investigated flight incidences using offline and hardware-in-the-loop simulation data.
• Jan. 2015 – Jan. 2017: Cornell University, Thermal Science Lab
  • Research Assistant
  • Advised by Prof. C. Thomas Avedisian
  • Research focused on fuel droplet combustion assessment, including burning rate and soot production for surrogate gasoline, jet fuel, and renewable jet fuel.

Skills

• Computer Languages: Python, C++, MATLAB
• Machine Learning and AI: physics-informed ML, graph neural networks, autoencoders, uncertainty quantification, optimization
• Physics Modeling and Simulation: CFD, VLM, FEA, LES, design automation, multi-fidelity analysis
• Engineering fundamentals: aerodynamics, numerical methods, finite element methods, combustion, soft robotics

Publications

Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using Graph Neural Networks

Shen, Y. (2026). "Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using Graph Neural Networks." Stanford University PhD Thesis.

A Multi-Fidelity Workflow for Conceptual Design Using Graph Neural Network-Based Field Prediction Surrogate Model

Shen, Y.; Needels, J.; Alonso, J. (2026). "A Multi-Fidelity Workflow for Conceptual Design Using Graph Neural Network-Based Field Prediction Surrogate Model." Computers & Fluids. 317, 107153.

A Multi-Fidelity Double-Delta Wing Dataset and Empirical Scaling Laws for GNN-based Aerodynamic Field Surrogates

Shen, Y.; Alonso, J. (2026). "A Multi-Fidelity Double-Delta Wing Dataset and Empirical Scaling Laws for GNN-based Aerodynamic Field Surrogates." AIAA SCITECH 2026.

Graph Neural Network-Guided Aerodynamic Shape Optimization for Conceptual Design of Supersonic Transport Wings

Shen, Y.; Alonso, J. (2025). "Graph Neural Network-Guided Aerodynamic Shape Optimization for Conceptual Design of Supersonic Transport Wings." AIAA Aviation 2025

ShapeBench: A Scalable Benchmark and Diagnostic Suite for Standardized Evaluation in Aerodynamic Shape Optimization

Fazliani, S.; Chawla, K.; Guo, J.; Shen, Y.; Ihme, M.; Udell, M. (2025). "ShapeBench: A Scalable Benchmark and Diagnostic Suite for Standardized Evaluation in Aerodynamic Shape Optimization." Preprint.

VortexNet: A Graph Neural Network-Based Multi-Fidelity Surrogate Model for Field Predictions

Shen, Y.; Needels, J.; Alonso, J. (2025). "VortexNet: A Graph Neural Network-Based Multi-Fidelity Surrogate Model for Field Predictions." AIAA SCITECH 2025. 1(0494).

Performance Evaluation of a Graph Neural Network-Augmented Multi-Fidelity Workflow for Predicting Aerodynamic Coefficients on Delta Wings at Low Speed

Shen, Y.; Alonso, J. (2025). "Performance Evaluation of a Graph Neural Network-Augmented Multi-Fidelity Workflow for Predicting Aerodynamic Coefficients on Delta Wings at Low Speed." AIAA SCITECH 2025..

Application of Multi-Fidelity Transfer Learning with Autoencoders for Efficient Construction of Surrogate Models

Shen, Y.; Patel, H.; Xu, Z.; Alonso, J. (2024). "Application of Multi-Fidelity Transfer Learning with Autoencoders for Efficient Construction of Surrogate Models." AIAA SCITECH 2024. AIAA 2024-0013.

Quantitative Investigation of Sooting Dynamics in Droplet Combustion Using an Automated Image Analysis Algorithm

Xu, Y.; Shen, Y.; Avedisian, C. T.; Hicks, M. C.; Choi, M. Y. (2023). "Quantitative investigation of sooting dynamics in droplet combustion using an automated image analysis algorithm." Fuel. 354, 129313.

Towards a Framework for Parametric Models and Geometric Sensitivity Computations with SU2 and pyCAPS

Shen, Y.; Patel, H.; Galbraith, M.; Alonso, J. (2023). "Towards a Framework for Parametric Models and Geometric Sensitivity Computations with SU2 and pyCAPS." AIAA Aviation 2023. AIAA 2023-3312.

Soft Robot Skin With Conformal Adaptability for On-Body Tactile Perception of Collaborative Robots

Ye, Z.; Pang, G.; Xu, K.; Hou, Z.; Lv, H.; Shen, Y.; Yang, G. (2022). "Soft Robot Skin With Conformal Adaptability for On-Body Tactile Perception of Collaborative Robots." IEEE Robotics and Automation Letters. 7, 5127-5134.

Quantitative Soot Volume Fraction Measurements from Droplet Combustion Experiments of Sooty Fuels

Xu, Y.; Shen, Y.; Avedisian, C. T.; Hicks, M.; Suh, H. K.; Choi, M. (2020). "Quantitative Soot Volume Fraction Measurements from Droplet Combustion Experiments of Sooty Fuels." 36th Annual Meeting of the American Society for Gravitational and Space Research (ASGSR).

Burning of Algae-Derived Biofuel Droplets and Their Mixtures with Jet Fuel

Xu, Y.; Brunson, J. D.; Avedisian, C. T.; Shen, Y.; Keresztes, I.; Condo, A. M.; Phillips, D. (2020). "Burning of Algae-Derived Biofuel Droplets and Their Mixtures with Jet Fuel." Energy & Fuels. 34, 929-935.

Comprehensive Study of the Initial Diameter for Combustion of n-Heptane/iso-Octane Mixture Droplets

Xu, Y.; Farouk, T.; Shen, Y.; Hicks, M. C.; Avedisian, C. T.; Xie, Y.; Reeves, A. P.; Dryer, F. L. (2017). "Comprehensive Study of the Initial Diameter for Combustion of n-Heptane/iso-Octane Mixture Droplets." 10th U.S. National Combustion Meeting.

Service and leadership

• Technical Committee Member, AIAA Multidisciplinary Design Optimization Technical Committee (2026-2027)
• Reviewer: AIAA Journal, Communications Engineering, Aerospace Science and Technology, Materials & Design, Engineering Applications of Artificial Intelligence, Computer-Aided Design, ASME Journal of Computing and Information Science in Engineering, and conference tracks including AIAA Aviation 2026
• Student research outreach: 2022 S.T. Yau High School Science Award mentoring and one-on-one research sessions with students on drone aerodynamics and morphing-wing design
• Awards: Cornell Engineering Learning Initiatives undergraduate research grant, best team poster award, and artistic merit award