ORCID: here, Google Scholar: here

Are you interested in collaboration? Please do not hesitate emailing Youngsoo!

Youngsoo is a computational math scientist in CASC under Computing directorate at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems, such as inverse problems, design optimization, and uncertainty quantification. His expertise includes various scientific computing disciplines as indicated in "Research interests" below. Together with his collaborators, he has developed various powerful model order reduction techniques, such as machine learning-based nonlinear manifold and space-time reduced order models for nonlinear dynamical systems. He has also developed the component-wise reduced order model optimization algorithm, which enables fast and accurate computational modeling tool for lattice-structure design. He is currently leading data-driven surrogate modeling development team for various physical simulations, with whom he developed the open source codes, libROM and LaghosROM. He is also involved with quantum computing research. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc at Sandia National Laboratories and Stanford University prior to joining LLNL in 2017.

YouTube/Virtual Conference/Seminar Talks [Recorded Videos]

• “Efficient physics-constrained data-driven physical simulations,”> SIG on Machine Learning and Dynamical Systems, Alan Turing Institute March 24, 2022.
• “Efficient nonlinear manifold reduced order models with sparse shallow neural networks,” BIGMECA international workshop at SafranTech, November 18, 2021.
• libROM: Library for physics-constrained data-driven physical simulations, DDPS seminar, LLNL, October 7, 2021.
• Physics-constrained data-driven methods of accurately accelerating simulations and their applications, PhIML Seminar, Pacific Northwest National Laboratory, August 16, 2021.
• Colloquium at Center for Mathematics and Artificial Intelligence, April 16, 2021.
• Analysis Junior Seminars at SISSA, March 19, 2021.
• Cornell MAE Spring 2021 Colloquium, February 16, 2021.
• Where are we with data-driven surrogate modeling for various physical simulations, January 19, 2021.
• Component-wise reduced order model with its application to lattice-structure design, 14th WCCM and Eccomas Congress, January 11, 2020.
• Efficient nonlinear manifold reduced order model, Machine Learning for Engineering Modeling, Simulation, and Design; Workshop at Neural Information Processing Systems, December 12, 2020.

libROM Tutorial Videos

• Poisson equation and its finite element discretization
• Poisson equation and its reduced order model
• Physics-informed sampling procedure for reduced order models
• Local reduced order models and interpolation-based parameterization
• Projection-based reduced order model for nonlinear system
• A complete derivation of dynamic mode decomposition(DMD)

Open-source codes

• libROM
• pylibROM
• LaghosROM
• scaleupROM
• LaSDI
• gLaSDI
• GPLaSDI

Research Interests

• Model order reduction
• Mathematical optimization
• Numerical linear algebra
• Numerical partial differential equation
• Scientific computing
• Machine learning
• Multidisciplinary design optimization (Topology optimization)
• Quantum computing

Patent

• Youngsoo Choi, Sean McBane, LLNL, Component-wise reduced-order model design optimization such as for lattice design optimization

Upcoming Presentations

• Invited speaker at AI4Differential Equations in Science Workshop, ICLR, Vienna, Austria, May 7 - 11, 2024.
• Minisymposium talk at ECCOMAS 2024, Lisbon, Portugal, June 3 - 7, 2024.
• An organizer of minisymposium on Model order reduction for parametrized continuum mechanics at 16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, Vancouver, British Columbia, Canada, July 21 - 26, 2024.
• Invited speaker at Model Reduction and Surrogate Modeling (MORe2024), La Jolla, California, September 9-13, 2024.

Presentations

• Minisymposium talk at SIAM Conference on Uncertainty Quantification (UQ24), Trieste, Italy, February 27 - March 1, 2024.
• Seminar talk at Numerical Analysis of Galerkin ROMs seminar series, February 6, 2024. YouTube link
• Invited talk at AI4Science AWS Seminar Series, January 30, 2024. 
• Talk and poster at Applied Mathematics and MMICCs Principal Investigator Meeting, January 8–10, 2024. 
• 10 min talk at CASC monthly division meeting, LLNL, December 14, 2023.
• A talk at CHaRMNET Annual meeting, Michigan State University, December 4-6, 2023.
• Keynote talk at Second Workshop on Physics Enhancing Machine Learning in Applied Mechanics, Institute of Physics, London, UK, November 20, 2023.
• Invited talk as Committee on Solid Earth Geophysics Fall 2023 Meeting, National Academies Sciences Engineering Medicine (NASEM), November 16, 2023. Note: my talk starts at 2:50:00 in this video
• FEM@LLNL Seminar Talk, Lawrence Livermore National Laboratory, November 7, 2023.
• Computational Research Leadership Council (CRLC) Seminar Series, California State University, Fresno, 9 AM PT, October 6, 2023.
• Applied and Computational Mathematics Seminar, GeorgiaTech, September 18, 2023.
• Numerical Analysis Seminar, The University of Hong Kong, September 13, 2023.
• 10th International Congress on Industrial and Applied Mathematics, Tokyo, Japan, August 20 - 25, 2023.
• DarkStar seminar talk on Journey to control flyer plate dynamics induced by multiple detonation, LLNL, August 15, 2023.
• 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, July 23 - 27, 2023.
• SIAM Conference on Optimization 2023, The Sheraton Grand Seattle, Seattle, Washington, U.S., May 31 - June 3, 2023.
• TAMIDS DTL/SciML Lab Seminar talk on physics-guided data-driven simulations, Texas A&M, April 7th, 2023. Recorded talk.
• AAAI Spring Symposium, Computational Approaches to Scientific Discovery on Certified and parameterized latent space dynamics identification for time dependent image data, Hyatt Regency, San Francisco Airport, California, March 27 - 29, 2023.
• Computational Approaches to Scientific Discovery, Hyatt Regency, San Francisco Airport, California, March 27 - 29, 2023.
• Applied Math Seminar, UC Santa Barbara, California, 3 PM PT, March 10, 2023.
• Computational Research Leadership Council (CRLC) Seminar Series, San Diego State University, California, 3:30 PM PT, February 3, 2023.
• e-Seminar on Scientific Machine Learning talk on Interpretable and physics-constrained data-driven methods for physical simulations, November 4, 2022.
• Data Science Seminar talk at LBL on Interpretable and physics-constrained data-driven methods for physical simulations, October 27, 2022.
• ML/AI Engineering Cohort hands-on Workshop talk on Interpretable and structure-preserving data-driven methods for physical simulations, October 20, 2022.
• SIAM Conference on Mathematics of Data Science talk on Nonlinear manifold reduced order models with shallow sparse neural network, September 30, 2022.
• Talk at Widely Applied Mathematics Seminar talk at Harvard University on Interpretable and physics-constrained data-driven methods for physical simulations, September 29, 2022.
• MMICC kick-off meeting talk on Interpretable and physics-constrained data-driven methods for physical simulations, September 29, 2022.
• WSC all hands meeting talk, LLNL on interpretable and explainable data-driven methods for physical simulations, September 28, 2022.
• Data Learning Working Group talk, Imperial College London on Interpretable and structure-preserving data-driven methods for physical simulations, September 20, 2022.
• Contributed talk at WCCM2022 on Nonlinear manifold to component-wise reduced order models towards multi-scale problems, July 31 - August 5, 2022.
• Contributed talk at SIAM Annual Meeting 2022 on Efficient Reduced Order Models for Fluid Dynamics: from linear to nonlinear manifold solution representation, July 11 - July 15, 2022.
• Contributed talk at WCCM-APCOM Yokohama 2022 on Nonlinear manifold to component-wise reduced order models towards multi-scale problems July 31 - August 5, 2022.
• MLDL workshop talk on gLaSDI: Parametric physics-informed greedy latent space dynamics identification on July 25th, 2022.
• HLCS seminar talk on Interpretable and Explainable Data-driven Methods for Physical Simulations July 5, 2022.
• Invited talk at IAP-UQ on "Latent Space Dynamics Learning," May 27, 2022.
• Contributed talk at SIAM Conference on Uncertainty Quantification 2022 on “Efficient hyper-reduced data-driven nonlinear manifold reduced order model,” April 13, 2022.
• Invited talk at SIG on Machine Learning and Dynamical Systems, Alan Turing Institute on “Efficient physics-constrained data-driven physical simulations,”> March 24, 2022.
• Invited talk at Colloquium on Artificial Intelligence Research and Optimization at Louisiana State University on “Physics-constrained data-driven physical simulations, using machine learning,” March 2, 2022.
• Invited talk at AIx/Digital Twin Seminar Series at Applied Materials on “Marrying Physics and Data to Accurately Accelerate Simulations,” March 1, 2022.
• Invited talk at Nuclear and Chemical Science (NACS) Seminar, LLNL on “Towards physics-constrained machine learning-based physical simulations,” February 16, 2022.
• Invited talk at MICDE/AIM Fall 2021 Seminar Series at University of Michigan on “Physics-constrained data-driven methods for accurately accelerating simulations,” December 3, 2021.
• Contributed talk at 33rd Nordic Seminar on Computational Mechanics on Nonlinear manifold to component-wise reduced order models towards multi-scale problems, November 25th, 2021.
• Contributed talk at BIGMECA international workshop at SafranTech on “Efficient nonlinear manifold reduced order models with sparse shallow neural networks,” November 18, 2021.
• Invited talk on “Reliable and generalizable data-driven physical simulations,” SGAI-AAAI-21, November 6, 2021.
• Seminar talk on “Data-driven methods of accelerating physical simulations and their applications” for CRLC (Computational Research Leadership Council) seminar series at the University of Houston Clear Lake, November 1, 2021.
• Seminar talk at DDPS seminar series, October 7, 2021.
• Contributed talk at mini-symposium (MS 4-9) on advances in reduced order modeling of solids, fluids and porous media, MMLDT-CSET 2021, September 26-29, 2021.
• Contributed talk at mini-symposium on model order reduction for physical simulations, 16th U.S. National Congress on Computational Mechanics, July 25-29, 2021.
• Contributed talk at WCSMO-14, June 13-18, 2021.
• Contributed talk at ECCOMAS Thematic Conference, Computational Sciences and AI in Industry, CSAI2021, June 7-9, 2021
• Seminar talk at Machine Learning + X seminar, CRUNCH group, Brown University, June 4, 2021.
• Contributed talk at IOP Institute of Physics, PETER 2021, May 25-27, 2021. (Please see the linked video from 1:40:00 to 3:03:38)
• Seminar talk at the Applied Mathematics Colloquium in the Institute of Analysis and Numerics, University of Muenster, May 19, 2021.
• Seminar talk at Thuerey Group, Technical University of Munich, May 6, 2021.
• Seminar talk at Colloquium at Center for Mathematics and Artificial Intelligence, April 16, 2021.
• Seminar talk at PMSL seminar, ExxonMobile, April 14, 2021.
• Seminar talk at CSC seminar, Max Planck Institute, April 13, 2021.
• Seminar talk at AJS - Analysis Junior Seminars, March 19, 2021.
• “A fast and accurate neural network reduced order model for advection-dominated physical simulations” at SIAM Conference on Computational Science and Engineering(CSE21), March 1-5, 2021, virtual conference.
• Seminar talk at MEMS (Mechanical Engineering & Materials Science) seminar at Duke University, February 3, 2021.
• Seminar talk at Cornell Sibley School Seminar Series at Cornell University, February 16, 2021.
• “Component-wise reduced order model lattice design” at 14th World Congress in Computational Mechanics and ECCOMAS Congress, January 11-15, 2021, virtual conference.
• “Efficient nonlinear manifold reduced order model” at Machine Learning for Engineering Modeling, Simulation, and Design, Workshop at Neural Information Processing Systems, December 12, 2020, virtual conference.
• “Space–time reduced order model for Boltzmann transport equation” at 26th International Conference on Transport Theory, September 22-27, 2019, Paris, France.
• “A practical space–time reduced order model for large-scale dynamical problems” at 15th U.S. National Congress on Computational Mechanics, July 28-August 1, 2019, Austin, Texas, USA.
• “Accelerating topology optimization process using reduced order models” at the World Congress of Structural and Multidisciplinary Optimization, May 20-24, 2019, Beijing, China.
• “Accelerating Training phase in time-dependent nonlinear model order reduction“ at SIAM Conference on Computational Science and Engineering, February 25 - March 1, 2019, Spokane, Washington, USA.
• “ST-GNAT and SNS: Model order reduction techniques for nonlinear dynamical systems.” Linear Algebra and Optimization Seminar at Stanford University (October 11, 2018).
• “ST-GNAT and SNS: Model order reduction techniques for time-dependent nonlinear system of equations.” Applied Mathematics Seminar at UC Berkeley/Lawrence Berkeley Laboratory, California (September 13, 2018).
• “Reduced representation for accelerating stress-constrained topology optimization.” 13th World Congress on Computational Mechanics, Marriot Marquis, New York City, NY (July 20-27, 2018).
• “Tensor decompositions in reduced order models.” Machine Learning Reading Group, LLNL, California (May 22, 2018).
• “Tensor decompositions in reduced order models.” SIAM Conference on Applied Linear Algebra, Hong Kong Baptist University (May 4-8, 2018).
• “Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction.” 2017 West Coast ROM workshop at LBNL, California (November 17, 2017).
• “Space–time least-squares Petrov–Galerkin projection in nonlinear model reduction.” PDE and Applied Math Seminar at UC Davis, California (October 12, 2017).
• “Gradient-based constrained optimization using a database of linear reduced-order models.” LLNL Design Optimization Symposium (September 22, 2017).
• “Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction.” at the 14th U.S. National Congress for Computational Mechanics, Montreal, Canada (July 17-20, 2017).
• “Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction.” Scientific computing and matrix computations seminar at UC Berkeley, California (April 12, 2017).

PostDoc., Sandia National Laboratories, Livermore, CA, 2015 – 2017

PostDoc., Farhat Research Group, Stanford, CA, 2013 – 2015

Ph.D., Computational and mathematical engineering, Stanford University, 2013

B.S., Civil and environmental engineering, Cornell University, 2007

A.S., Engineering, Montgomery County Community College, 2004

• Kim, Y., Choi, Y., & Yoo, B. (2024). "Gappy AE: A nonlinear approach for Gappy data reconstruction using auto-encoder," Computer Methods in Applied Mechanics and Engineering, Volume 426, 116978.
• Diaz, A.N., Choi, Y., & Heinkenschloss, M. (2024). "A fast and accurate domain-decomposition nonlinear manifold reduced order model," Computer Methods in Applied Mechanics and Engineering, Volume 425, 116943.
• Bonneville, C., Choi, Y., Ghosh, D., & Belof, J.L. (2024). "GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder," Computer Methods in Applied Mechanics and Engineering, Volume 418, Part A, 116535.
• Kim, Y., Choi, Y., & Yoo, B. (2023). "Gappy data reconstruction using unsupervised learning for digital twin," arXiv preprint, arXiv:2312.07902.
• Chung, S.W., Choi, Y., Roy, R., Moore, T., Roy, T., Lin, T.Y., Nguyen, D.Y., Hahn, C., Duoss, E.B., & Baker, S.E. (2023). "Train Small, Model Big: Scalable Physics Simulators via Reduced Order Modeling and Domain Decomposition," arXiv preprint, arXiv:2401.10245.
• Bonneville, C., Choi, Y., Ghosh, D., & Belof, J.L. (2023). "Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster.
• Diaz, A.N., Choi, Y., & Heinkenschloss, M. (2023). "Nonlinear-manifold reduced order models with domain decomposition," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster and Video.
• Suh, S.W., Chung, S.W., Bremer, P.T., & Choi, Y. (2023). "Accelerating Flow Simulations using Online Dynamic Mode Decomposition," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster and Video.
• Chung, S.W., Choi, Y., Roy, P., Moore, T., Roy, T., Lin, T., & Baker, S.E. (2023). "Scalable physics-guided data-driven component model reduction for Stokes flow," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster.
• Brown, A.L., Chin, E.B., Choi, Y., Khairallah, S.A., & McKeown, J.T. (2023). "A Data-Driven, Non-Linear, Parameterized Reduced Order Model of Metal 3D Printing," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster and Video.
• Wen, T., Lee, K., & Choi, Y. (2023). "Reduced-order modeling for parameterized PDEs via implicit neural representations," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster and Video.
• Tsai, P.H., Chung, S.W., Ghosh, D., Loffeld, J., Choi, Y., & Belof, J.L. (2023). "Accelerating Kinetic Simulations of Electrostatic Plasmas with Reduced-Order Modeling," Machine Learning and the Physical Sciences Workshop, NeurIPS 2023. Poster and Video.
• Tran, A., He, X., Messenger, D.A., Choi, Y., & Bortz, D.M. (2023). "Weak-Form Latent Space Dynamics Identification," arXiv preprint, arXiv:2311.12880.
• Teeratorn, K., O'Malley, D., Choi, Y., Viswanathan, H.S., & Yoon, H. (2023). "Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer," arXiv preprint, arXiv:2310.03770.
• Vales, C., Choi, Y., Copeland, D.M., & Cheung, S.W. (2023). "Energy conserving quadrature based dimensionality reduction for nonlinear hydrodynamics problems," Technical Report, LLNL-TR-853055.
• Kadeethum, T., Jakeman, J.D., Choi, Y., Bouklas, N., & Yoon, H. (2023). "Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems," IEEE Access.
• He, X., Choi, Y., Fries, W.D., Belof, J., & Chen, J.S. (2023). "gLaSDI: parametric physics-informed greedy latent space dynamics identification," Journal of Computational Physics, 112267.
• Cheung, S.W., Choi, Y., Springer, H.K., & Kadeethum, T. (2023). "Data-scarce surrogate modeling of shock-induced pore collapse process," arXiv preprint, arXiv:2306.00184.
• Huhn, Q.A., Tano, M.E., Ragusa, J.C., & Choi, Y. (2023). "Parametric dynamic mode decomposition for reduced order modeling," Journal of Computational Physics, Volume 475, p111852.
• Petra, C.G., Salazar de Troya, M.A., Petra, P., Choi, Y., Oxberry, G.M., & Tortorelli, D. (2022). “On the implementation of a quasi-Newton interior-point algorithm in function spaces using finite element discretizations,” Optimization Methods and Software.
• Cheung, S. W., Choi, Y., Copeland, D., & Huynh, K. (2023). "Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition," Journal of Computational Physics, 472, p111655.
• Kadeethum, T., Ballarin, F., O'Malley, D., Choi, Y., Bouklas, N., & Yoon, H. (2022). "Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning," Scientific reports.
• He, X., Choi, Y., Fries, W.D., Belof, J., & Chen, J.S. "Certified data-driven physics-informed greedy auto-encoder simulator," Machine Learning and the Physical Sciences workshop, NerIPS 2022.
• McBane, S., Choi, Y., & Willcox, K. (2022). "Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models," Computer Methods in Applied Mechanics and Engineering, Volume 400, p115525.
• Kadeethum, T., O'Malley, D., Choi, Y., Viswanathan, S., Bouklas, N., & Yoon, H. (2022). "Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties," Computers and Geosciences, 105212.
• Fries, W.D., He, X., & Choi, Y. (2022). "LaSDI: parametric latent space dynamics identification," Computer Methods in Applied Mechanics and Engineering, 115436.
• Jekel, C.F., Sterbentz, D.M., Aubry, S., Choi, Y., White, D.A., Belof, J.L., (2022). “Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-Meshkov Instabilities,” arXiv preprint, arXiv:2208.11477.
• Lauzon, J.T., Cheung, S.W., Shin, Y., Choi, Y., Copeland, D.M., & Huynh, K. (2022). "S-OPT: a points selection algorithm for hyper-reduction in reduced order models," arXiv preprint, arXiv:2203.16494.
• Kim, Y., Choi, Y., Widemann, D., & Zohdi, T. (2022). “A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder,” Journal of Computational Physics, 110841.
• Copeland, D., Cheung, S. W., Huynh, K., & Choi, Y. (2022). “Reduced order models for Lagrangian hydrodynamics,” Computer Methods in Applied Mechanics and Engineering, 388, 114259.
• Kadeethum, T., O’Malley, D., Fuhg, J.N., Choi, Y., Lee, J., Viswanathan, H.S., & Bouklas, N. (2021). A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Nature Computational Science, 1, 819–829. doi.org/10.1038/s43588-021-00171-3
• Hoang, C., Choi, Y., & Carlberg, K. (2021). Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction. Computer Methods in Applied Mechanics and Engineering, Volume 384.
• McBane, S. & Choi, Y. (2021). Component-wise reduced order model lattice-type structure design. Computer Methods in Applied Mechanics and Engineering, 381, 113813.
• Kadeethum, T., Ballarin, F., Choi, Y., O'Malley, D., Yoon, H., & Bouklas, N. (2021). Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. Advances in Water Resources, Volume 160.
• Kim, Y., Wang, K., Choi, Y. (2021). Efficient space–time reduced order model for linear dynamical systems in Python using less than 120 lines of code. Mathematics,
• Petersson, N.A., Garcia, F., Guenther, S., Choi, Y. (2021). IBM Open Science Price-SWAP Gate Challenge. Technical Report, LLNL-TR-821599.
• Choi, Y., Boncoraglio, G., Anderson, S., Amsallem, D., & Farhat, C. (2020). Gradient-based constrained optimization using a database of linear reduced-order models. Journal of Computational Physics, Volume 423, 109787.
• Anders, P., Garcia, F., Appelo, D., Guenther, S., Choi, Y., & Vogt, R. (2020). Quantum Physics without the Physics. arXiv preprint, arXiv:2012.03865.
• Kim, Y., Choi, Y., Widemann, D., & Zohdi, T. (2020). Efficient nonlinear manifold reduced order model. 2020 Conference on Neural Information Processing Systems.
• White, D., Choi, Y., & Kudo, J. (2020). A dual mesh method with adaptivity for stress constrained topology optimization.Structural and Multidisciplinary Optimization, 61:749–762
• Choi, Y., Coombs, D., & Anderson, R. (2020). SNS: A Solution-based Nonlinear Subspace method for time-dependent nonlinear model order reduction. SIAM Journal of Scientific Computing, 42(2), A1116–A1146.
• Choi, Y., Brown, P., Arrighi, B., Anderson, R., & Huynh, K. (2021). Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems. Journal of Computational Physics, Volume 424, 109845.
• Choi, Y., Oxberry, G., White, D., & Kirchdoerfer, T. (2019). Accelerating topology optimization using reduced order models. World Congress of Structural and Multidisciplinary Optimization.
• Choi, Y. & Carlberg, K. (2019). Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction. SIAM Journal on Scientific Computing, 41(1):A26-A58.
• Carlberg, K., Choi, Y., & Sargsyan, S. (2018). Conservative model reduction for finite-volume models. Journal of Computational Physics, 371, 280-314.
• Choi, Y., Farhat, C., Murray, W., & Saunders, M. (2015). A practical factorization of a Schur complement for PDE-constrained distributed optimal control. Journal of Scientific Computing, 65(2), 576-597.
• Amsallem, D., Neumann, D., Choi, Y., & Farhat, C. (2015). Linearized Aeroelastic Computations in the Frequency Domain Based on Computational Fluid Dynamics. arXiv preprint, arXiv:1506.07441.
• Amsallem, D., Zahr, M., Choi, Y., & Farhat, C. (2015). Design optimization using hyper-reduced-order models. Structural and Multidisciplinary Optimization, 51(4), 919-940.
• Choi, Y. (2012). Simultaneous analysis and design in PDE-constrained optimization (Doctoral dissertation, Stanford University).
• Wein, L. M., Choi, Y., & Denuit, S. (2010). Analyzing Evacuation Versus Shelter-in-Place Strategies After a Terrorist Nuclear Detonation. Risk Analysis, 30(9), 1315-1327.

• Director's Science and Technology Excellence in Publication Award at LLNL in 2023
• Best Instructor Award from the AHPCRC summer institute at Stanford University in 2015
• Dean’s Office Award for the Stanford Opportunity Job Fair Research Poster in 2013
• Best Instructor Award from the AHPCRC summer institute at Stanford University in 2011
• Best Paper Award from the Society for Risk Analysis in 2010
• Recipient of Stanford ICME Ph.D Fellowship in 2007
• Elaine and Herman Blumenthal Award for Academic Excellence
• The Mathematics Faculty Award for Excellence
• CRLA Regular Tutoring Certificate
• A member of Phi Theta Kappa Honor Society
• A member of Golden Key Honor Society
• A member of Tau Beta Pi Engineering Honor Society