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YUNAN YANG


ETH Zurich / Alessandro Della Bella

I am a tenure-track Goenka Family Assistant Professor at the Department of Mathematics, Cornell University.

I obtained my Ph.D. in 2018 from the Department of Mathematics, UT Austin, supervised by Prof. Björn Engquist. I was a Courant Instructor at Courant Institute of Mathematical Sciences, NYU, from 09/2018 to 08/2021, and a Simons-Berkeley Research Fellow at Simons Institute for the Theory of Computing in Fall 2021. I was an Advanced Fellow at the Institute for Theoretical Studies, ETH Zürich from 01/2022 to 06/2023.

I am interested in Inverse Problems, Numerical Analysis, Nonconvex Optimization, Optimal Transport, and Machine Learning. Here is my Curriculum Vitae.

Contact Information

Office: Malott Hall 582, Tower Rd, Ithaca, NY 14853, USA
Email: yunan.yang@cornell.edu

Please do not hesitate to contact me if our paths may cross.

RESEARCH

is formalized curiosity. It is poking and prying with a purpose. — Zora Neale Hurston

Extensions and Applications of Optimal Transport

The optimal mass transport problem seeks the most efficient way of transforming one mass distribution to the other relative to a given cost function. It was first brought up by Monge in 1781 and later expanded by Kantorovich. The topic of optimal transportation has been a prominent subject of study for several decades, owing to the sound theoretical foundation established over the past two centuries through mathematical analysis. One of my main research interests is exploring the potential of optimal transport in Applied Mathematics. My PhD thesis work was on this topic.

Here is a list of my work focusing on the application and extension on the topic of optimal transportation, especially on seismic inversion, e.g., Full-Waveform Inversion (FWI):

  • Bao, C., Li, Z. and Yang, Y., 2025. Well-Posedness and Efficient Algorithms for Inverse Optimal Transport with Bregman Regularization. arXiv preprint arXiv:2510.03803. [pdf]
  • Zeng, Z., Neumann, M. and Yang, Y., 2025. Robust Frequency Domain Full-Waveform Inversion via HV Geometry. IEEE Transactions on Computational Imaging, 11, pp.1271-1282.
  • Botvinick-Greenhouse, J., Yang, Y., Maulik, R., 2023. Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit. Chaos 33 (10): 103108. [pdf]
  • Han, R., Slepčev, D., Yang, Y., 2023. HV Geometry for Signal Comparison. To Appear in Quarterly of Applied Mathematics. [pdf]
  • Yang, Y., Nurbekyan, L., Negrini, E., Martin, R. and Pasha, M., 2023. Optimal transport for parameter identification of chaotic dynamics via invariant measures. SIAM Journal on Applied Dynamical Systems, 22(1):269–310, 2023. [pdf]
  • Frederick, C. and Yang, Y., 2022. Snapshots of modern mathematics from Oberwolfach, Mathematisches Forschungsinstitut Oberwolfach, 2022-04. [pdf]
  • Engquist, B. and Yang, Y., 2022. Optimal Transport Based Seismic Inversion: Beyond Cycle Skipping. Communications on Pure and Applied Mathematics. [pdf]
  • Dunlop, M. and Yang, Y., 2021. Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion. SIAM/ASA Journal on Uncertainty Quantification, 9(4), pp.1499-1526. [pdf]
  • Engquist, B., Ren, K. and Yang, Y., 2020. The quadratic Wasserstein metric for inverse data matching. Inverse Problems, 36(5), p.055001. [pdf]
  • Engquist, B. and Yang, Y., 2019. Seismic imaging and optimal transport. Communications in Information and Systems, Vol. 19, No. 2 (2019), pp. 95-145 [pdf]
  • Engquist, B. and Yang, Y., 2019. Seismic inversion and the data normalization for optimal transport. Methods and Applications of Analysis, Vol. 26, No. 2 (2019), pp. 133-148. [pdf]
  • Yang, Y. and Engquist, B., 2018. Analysis of optimal transport and related misfit functions in full-waveform inversion. Geophysics, 83(1), pp.A7-A12. [pdf]
  • Yang, Y., Engquist, B., Sun, J. and Hamfeldt, B.F., 2018. Application of optimal transport and the quadratic Wasserstein metric to full-waveform inversion. Geophysics, 83(1), pp.R43-R62. [pdf]
  • Engquist, B., Froese, B.D. and Yang, Y., 2016. Optimal transport for seismic full waveform inversion. Communications in Mathematical Science, 14(8):2309-2330, 2016. [pdf]

Inverse Problems

Studying Inverse Problems is like solving a puzzle. You are given a partial clue about the ground truth (i.e., data and model), and you must figure out the answer following the clue. Sometimes, we are concerned about whether it is solvable or not (i.e., the problem's ill-/well-posedness). Sometimes, we are interested in developing a way to find the solution (computational inverse problem). Different inverse problems exhibit totally different properties, making the entire subject extremely fun to study! Here is a list of my related work on Inverse Problems (minus those solved using Optimal Transport):

  • van Leeuwen, T. and Yang, Y., 2025. An analysis of constraint-relaxation in PDE-based inverse problems. Inverse Problems, 41(2), p.025009.
  • Li, Q., Oprea, M., Wang, L. and Yang, Y., 2025. Inverse Problems Over Probability Measure Space. arXiv preprint arXiv:2504.18999. [pdf]
  • Einkemmer, L., Li, Q., Wang, L. and Yang, Y., 2023. Suppressing Instability in a Vlasov-Poisson System by an External Electric Field Through Constrained Optimization. Journal of Computational Physics, 498, p.112662. [pdf]
  • Li, Q., Wang, L. and Yang, Y., 2023. Differential-Equation Constrained Optimization With Stochasticity. submitted; arXiv preprint arXiv:2305.04024. [pdf]
  • Mahankali, S. and Yang, Y., 2023. Norm-dependent convergence and stability of the inverse scattering series for diffuse and scalar waves. Inverse Problems, 39(5), p.054005. [pdf]
  • Zhu, B., Hu, J., Lou, Y. and Yang, Y., 2023. Implicit Regularization Effects of the Sobolev Norms in Image Processing. La Matematica (2023). [pdf]

Optimization Algorithms

Many challenging inverse problems, optimal control, and optimal design tasks are eventually translated into optimization problems to be solved on the computer. Most of them are nonlinear, and the resulting optimization problems suffer from severe nonconvexity, for example, PDE-constrained optimization problems and machine learning training. My interests in designing optimization algorithms originated from dealing with these challenges. The main ideas are to design algorithms (1) for global optimization (i.e., the iterates converge to the global minimizer), (2) for faster convergence with the minimal computational cost, and (3) to incorporate the special property of forward model (e.g., the modeling PDE, or specific NN architecture) into the choice of optimization algorithms. Here is a list of my related work:

  • Liu, H., Nurbekyan, L., Tian, X. and Yang, Y., 2025. Adaptive Preconditioned Gradient Descent with Energy. Communications in Mathematical Sciences, 23(5), pp.1413-1446.
  • Li, Q., Wang, L. and Yang, Y., 2024. Differential-Equation Constrained Optimization with Stochasticity. SIAM/ASA Journal on Uncertainty Quantification, 12(2), pp.549-578.
  • Engquist, B., Ren, K. and Yang, Y., 2024. Adaptive State-Dependent Diffusion for Derivative-Free Optimization. Commun. Appl. Math. Comput. (2024). [pdf]
  • Engquist, B., Ren, K. and Yang, Y., 2022. An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization. submitted; arXiv preprint arXiv:2204.05923. [pdf]
  • Nurbekyan, L., Lei, W. and Yang, Y., 2023. Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems. SIAM Journal on Scientific Computing, 45(4), pp.A1621-A1655. [pdf]

Scientific Machine Learning

Machine Learning has immense potential and is a crucial subject in today's world. There are many similarities between Machine Learning concepts and other areas of Applied Mathematics, and understanding these connections is key to a comprehensive understanding of Machine Learning. Additionally, Machine Learning can be utilized to tackle persistent challenges in various domains of Applied Mathematics. Here is a list of my related work on understanding and utilizing Machine Learning:

  • Guerra, N., Nelsen, N.H. and Yang, Y., 2025. Learning Where to Learn: Training Distribution Selection for Provable OOD Performance. arXiv preprint arXiv:2505.21626. [pdf]
  • Nelsen, N.H. and Yang, Y., 2025. Operator Learning Meets Inverse Problems: A Probabilistic Perspective. To appear in Handbook of Numerical Analysis.
  • Wu C., Song R., Liu C., Yang, Y., Li, A., Huang, M. Geng T., 2024. NP-GL: Extending Power of Nature from Binary Problems to Real-World Graph Learning. To appear in The 12th International Conference on Learning Representations (ICLR) 2024.
  • Liu, Z., Yang, Y., Pan, Z., Sharma A., Hasan, A., Ding, C., Li A., Huang, M., and Geng, T., 2023. Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning. The 60th Design Automation Conference (DAC). [pdf]
  • Molinaro, R., Yang, Y., Engquist, B. and Mishra, S., 2023. Neural Inverse Operators for Solving PDE Inverse Problems. Proceedings of the 40th International Conference on Machine Learning, PMLR 202:25105-25139, 2023. [pdf]
  • Yu, A.,Yang, Y. and Townsend, A., 2023. Tuning Frequency Bias in Neural Network Training with Nonuniform Data. The 11th International Conference on Learning Representations (ICLR) 2023. [pdf]
  • Engquist, B., Ren, K. and Yang, Y., 2022. A Generalized Weighted Optimization Method for Computational Learning and Inversion. The 10th International Conference on Learning Representations (ICLR) 2022. [pdf]

Adjoint Monte Carlo Method and Optimization

In a sequence of works, I investigate how to mathematically "differentiate" a Monte Carlo method, which is random in nature, with respect to parameters of interest. In particular, I studied such Monte Carlo methods designed to solve certain Partial Differential Equations (PDEs), including the Radiative Transport Equation (RTE) and the Boltzmann Equation. Here is a list of my related work:

  • Caflisch, R. and Yang, Y., 2024. Adjoint Monte Carlo Method. In: Carrillo, J.A., Tadmor, E. (eds) Active Particles, Volume 4. Modeling and Simulation in Science, Engineering and Technology. Birkhauser, Cham.
  • Caflisch, R., and Yang, Y., 2024. Adjoint DSMC for nonlinear Boltzmann equation constrained optimization. submitted; arXiv preprint arXiv:2401.08361. [pdf]
  • Li, Q., Wang, L., and Yang, Y., 2022. Monte Carlo Gradient in Optimization Constrained by Radiative Transport Equation. SIAM Journal on Numerical Analysis, 61(6), pp.2744-2774.[pdf]
  • Yang, Y., Silantyev, D. and Caflisch, R., 2023. Adjoint DSMC for Nonlinear Spatially-Homogeneous Boltzmann Equation With a General Collision Model. Journal of Computational Physics, p.112247. [pdf]
  • Caflisch, R., Silantyev, D. and Yang, Y., 2021. Adjoint DSMC for nonlinear Boltzmann equation constrained optimization. Journal of Computational Physics, 439, p.110404. [pdf]

Dynamical System

Working with dynamical systems is always a humbling experience as the complexity of the trajectory behavior is so rich and case-dependent. However, there are many aspects to explore, especially from a data-driven perspective. Here is list of my related work on data-driven approaches for learning dynamical systems:

  • Oprea, M., Townsend, A. and Yang, Y., 2025. The Distributional Koopman Operator for Random Dynamical Systems. Mathematics of Control, Signals, and Systems, 37(4), pp.769-798.
  • Botvinick-Greenhouse, J., Martin, R. and Yang, Y., 2025. Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification. Physical Review Letters, 135(16), p.167202.
  • Botvinick-Greenhouse, J., Yang, Y., Maulik, R., 2023. Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit. Chaos 33 (10): 103108. [pdf]
  • Botvinick-Greenhouse, J., Martin, R. and Yang, Y., 2023. Learning dynamics on invariant measures using PDEconstrained optimization. Chaos 33, 063152 (2023) [pdf]
  • Yang, Y., Nurbekyan, L., Negrini, E., Martin, R. and Pasha, M., 2023. Optimal transport for parameter identification of chaotic dynamics via invariant measures. SIAM Journal on Applied Dynamical Systems, 22(1):269–310, 2023. [pdf]

PUBLICATION

For the complete and most up-to-date list, see Google Scholar (and my CV).

Preprints

  1. Mokhtari, Y., Frederick, C., Yang, Y. and Engquist, B., 2026. Velocity Reconstruction from Flow-Induced Magnetic Fields. arXiv preprint arXiv:2602.22097. [pdf]
  2. Bao, C., Li, Z. and Yang, Y., 2025. Well-Posedness and Efficient Algorithms for Inverse Optimal Transport with Bregman Regularization. arXiv preprint arXiv:2510.03803. [pdf]
  3. Espinosa, D.S., Thiede, E.H. and Yang, Y., 2025. Cryo-EM as a Stochastic Inverse Problem. arXiv preprint arXiv:2509.05541. [pdf]
  4. Neumann, M. and Yang, Y., 2025. HV Metric for Time-Domain Full Waveform Inversion. arXiv preprint arXiv:2508.17122. [pdf]
  5. Guerra, N., Nelsen, N.H. and Yang, Y., 2025. Learning Where to Learn: Training Distribution Selection for Provable OOD Performance. arXiv preprint arXiv:2505.21626. [pdf]
  6. Li, Q., Oprea, M., Wang, L. and Yang, Y., 2025. Inverse Problems Over Probability Measure Space. arXiv preprint arXiv:2504.18999. [pdf]
  7. Engquist, B., Ren, K. and Yang, Y., 2024. Sampling with Adaptive Variance for Multimodal Distributions. arXiv preprint arXiv:2411.15220. [pdf]

Refereed Journal Articles

  1. Botvinick-Greenhouse, J., Oprea, M., Maulik, R. and Yang, Y., 2025. Measure-Theoretic Time-Delay Embedding. Journal of Statistical Physics, 192(12). [pdf]
  2. Oprea, M., Townsend, A. and Yang, Y., 2025. The Distributional Koopman Operator for Random Dynamical Systems. Mathematics of Control, Signals, and Systems, 37(4), pp.769-798. [pdf]
  3. Zeng, Z., Neumann, M. and Yang, Y., 2025. Robust Frequency Domain Full-Waveform Inversion via HV Geometry. IEEE Transactions on Computational Imaging, 11, pp.1271-1282. [pdf]
  4. Botvinick-Greenhouse, J., Martin, R. and Yang, Y., 2025. Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification. Physical Review Letters, 135(16), p.167202. [pdf]
  5. Alaifari, R. and Yang, Y., 2025. Multi-Window Approaches for Direct and Stable STFT Phase Retrieval. SIAM Journal on Applied Mathematics, 85(5), pp.2376-2398. [pdf]
  6. Engquist, B., Ren, K. and Yang, Y., 2025. An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization. Communications in Mathematical Sciences, 23(6), pp.1669-1703. [pdf]
  7. Liu, H., Nurbekyan, L., Tian, X. and Yang, Y., 2025. Adaptive Preconditioned Gradient Descent with Energy. Communications in Mathematical Sciences, 23(5), pp.1413-1446. [pdf]
  8. van Leeuwen, T. and Yang, Y., 2025. An analysis of constraint-relaxation in PDE-based inverse problems. Inverse Problems, 41(2), p.025009. [pdf]
  9. Li, Q., Wang, L. and Yang, Y., 2024. Differential-Equation Constrained Optimization with Stochasticity. SIAM/ASA Journal on Uncertainty Quantification, 12(2), pp.549-578. [pdf]
  10. Einkemmer, L., Li, Q., Wang, L. and Yang, Y., 2024. Suppressing Instability in a Vlasov-Poisson System by an External Electric Field Through Constrained Optimization. Journal of Computational Physics, 498, p.112662. [pdf]
  11. Engquist, B., Ren, K. and Yang, Y., 2024. Adaptive State-Dependent Diffusion for Derivative-Free Optimization. Communications on Applied Mathematics and Computation, 6(2), pp.1241-1269. [pdf]
  12. Han, R., Slepcev, D. and Yang, Y., 2024. HV geometry for signal comparison. Quarterly of Applied Mathematics, 82(2), pp.391-430. [pdf]
  13. Zhu, B., Hu, J., Lou, Y. and Yang, Y., 2024. Implicit regularization effects of the Sobolev norms in image processing. La Matematica, 3(1), pp.79-107. [pdf]
  14. Botvinick-Greenhouse, J., Yang, Y. and Maulik, R., 2023. Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(10), 103108. [pdf]
  15. Li, Q., Wang, L. and Yang, Y., 2023. Monte Carlo Gradient in Optimization Constrained by Radiative Transport Equation. SIAM Journal on Numerical Analysis, 61(6), pp.2744-2774. [pdf]
  16. Botvinick-Greenhouse, J., Martin, R. and Yang, Y., 2023. Learning dynamics on invariant measures using PDE-constrained optimization. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(6). [pdf]
  17. Yang, Y., Silantyev, D. and Caflisch, R., 2023. Adjoint DSMC for nonlinear spatially-homogeneous Boltzmann equation with a general collision model. Journal of Computational Physics, p.112247. [pdf]
  18. Mahankali, S. and Yang, Y., 2023. Norm-dependent convergence and stability of the inverse scattering series for diffuse and scalar waves. Inverse Problems, 39(5), p.054005. [pdf]
  19. Nurbekyan, L., Lei, W. and Yang, Y., 2023. Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems. SIAM Journal on Scientific Computing, 45(4), pp.A1621-A1655. [pdf]
  20. Yang, Y., Nurbekyan, L., Negrini, E., Martin, R. and Pasha, M., 2023. Optimal transport for parameter identification of chaotic dynamics via invariant measures. SIAM Journal on Applied Dynamical Systems, 22(1), pp.269-310. [pdf]
  21. Frederick, C. and Yang, Y., 2022. Seeing through rock with help from optimal transport. Snapshots of modern mathematics from Oberwolfach, 2022-04. [pdf]
  22. Engquist, B. and Yang, Y., 2022. Optimal transport based seismic inversion: Beyond cycle skipping. Communications on Pure and Applied Mathematics, 75(10), pp.2201-2244. [pdf]
  23. Yang, Y., Townsend, A. and Appelo, D., 2022. Anderson Acceleration Based on the H-s Sobolev Norm for Contractive and Noncontractive Fixed-Point Operators. Journal of Computational and Applied Mathematics, 403, p.113844. [pdf]
  24. Dunlop, M. and Yang, Y., 2021. Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion. SIAM/ASA Journal on Uncertainty Quantification, 9(4), pp.1499-1526. [pdf]
  25. Caflisch, R., Silantyev, D. and Yang, Y., 2021. Adjoint DSMC for nonlinear Boltzmann equation constrained optimization. Journal of Computational Physics, 439, p.110404. [pdf]
  26. Yang, Y., 2021. Anderson acceleration for seismic inversion. Geophysics, 86(1), pp.R99-R108. [pdf]
  27. Engquist, B., Ren, K. and Yang, Y., 2020. The quadratic Wasserstein metric for inverse data matching. Inverse Problems, 36(5), p.055001. [pdf]
  28. Engquist, B. and Yang, Y., 2019. Seismic imaging and optimal transport. Communications in Information and Systems, 19(2), pp.95-145. [pdf]
  29. Engquist, B. and Yang, Y., 2019. Seismic inversion and the data normalization for optimal transport. Methods and Applications of Analysis, 26(2), pp.133-148. [pdf]
  30. Yang, Y. and Engquist, B., 2018. Analysis of optimal transport and related misfit functions in full-waveform inversion. Geophysics, 83(1), pp.A7-A12. [pdf]
  31. Yang, Y., Engquist, B., Sun, J. and Hamfeldt, B.F., 2018. Application of optimal transport and the quadratic Wasserstein metric to full-waveform inversion. Geophysics, 83(1), pp.R43-R62. [pdf]
  32. Engquist, B., Froese, B.D. and Yang, Y., 2016. Optimal transport for seismic full waveform inversion. Communications in Mathematical Science, 14(8), pp.2309-2330. [pdf]

Refereed Conference Proceedings

  1. Wu, C., Song, R., Liu, C., Yang, Y., Li, A., Huang, M. and Geng, T., 2024. NP-GL: Extending Power of Nature from Binary Problems to Real-World Graph Learning. The 12th International Conference on Learning Representations (ICLR) 2024. [pdf]
  2. Molinaro, R., Yang, Y., Engquist, B. and Mishra, S., 2023. Neural Inverse Operators for Solving PDE Inverse Problems. Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR 202:25105-25139. [pdf]
  3. Liu, Z., Yang, Y., Pan, Z., Sharma, A., Hasan, A., Ding, C., Li, A., Huang, M. and Geng, T., 2023. Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning. The 60th Design Automation Conference (DAC). [pdf]
  4. Yu, A., Yang, Y. and Townsend, A., 2023. Tuning Frequency Bias in Neural Network Training with Nonuniform Data. The 11th International Conference on Learning Representations (ICLR) 2023. [pdf]
  5. Engquist, B., Ren, K. and Yang, Y., 2022. A Generalized Weighted Optimization Method for Computational Learning and Inversion. The 10th International Conference on Learning Representations (ICLR) 2022. [pdf]
  6. Yang, Y., 2020. Anderson acceleration for seismic inversion. In SEG Technical Program Expanded Abstracts 2020 (pp. 880-884). Society of Exploration Geophysicists. [pdf]
  7. Dunlop, M. and Yang, Y., 2020. New likelihood functions and level-set prior for Bayesian full-waveform inversion. In SEG Technical Program Expanded Abstracts 2020 (pp. 825-829). Society of Exploration Geophysicists. [pdf]
  8. Yang, Y. and Engquist, B., 2019. Improving optimal transport based FWI through data normalization. In SEG Technical Program Expanded Abstracts 2019 (pp. 1315-1319). Society of Exploration Geophysicists. [pdf]
  9. Yang, Y. and Engquist, B., 2018. Model recovery below reflectors by optimal-transport FWI. In SEG Technical Program Expanded Abstracts 2018 (pp. 1178-1182). Society of Exploration Geophysicists. [pdf]
  10. Ramos-Martinez, J., Qiu, L., Kirkebo, J., Valenciano, A.A. and Yang, Y., 2018. Long-wavelength FWI updates beyond cycle skipping. In SEG Technical Program Expanded Abstracts 2018 (pp. 1168-1172). Society of Exploration Geophysicists. [pdf]
  11. Yang, Y. and Engquist, B., 2017. Analysis of optimal transport and related misfit functions in full-waveform inversion. In SEG Technical Program Expanded Abstracts 2017 (pp. 1291-1296). Society of Exploration Geophysicists. [pdf]
  12. Qiu, L., Ramos-Martinez, J., Valenciano, A., Yang, Y. and Engquist, B., 2017. Full-waveform inversion with exponentially-encoded optimal transport norm. In SEG Technical Program Expanded Abstracts 2017 (pp. 1286-1290). Society of Exploration Geophysicists. [pdf]
  13. Yang, Y., Engquist, B. and Sun, J., 2016. Convexity of the quadratic Wasserstein metric as a misfit function for full-waveform inversion. In SEG Technical Program Expanded Abstracts 2016 (pp. 1385-1389). Society of Exploration Geophysicists. [pdf]

Book Chapters

  1. Nelsen, N.H. and Yang, Y., 2025. Operator Learning Meets Inverse Problems: A Probabilistic Perspective. To appear in Handbook of Numerical Analysis. [pdf]
  2. Caflisch, R. and Yang, Y., 2024. Adjoint Monte Carlo Method. In: Carrillo, J.A., Tadmor, E. (eds) Active Particles, Volume 4. Modeling and Simulation in Science, Engineering and Technology. Birkhauser, Cham. [pdf]
  3. Yang, Y. and Engquist, B., 2017. Analysis of optimal transport related misfit functions in seismic imaging. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. Lecture Notes in Computer Science, vol 10589. Springer, Cham. [pdf]
×

TEACHING

  • @ NYU
  • 2020 Fall MATH-UA.140 Linear Algebra
  • 2020 Summer MATH-UA.252 Numerical Analysis
  • 2020 Spring MATH-UA.122 Calculus II
  • 2019 Spring & Fall MATH-UA.120 Discrete Math
  • 2018 Fall MATH-UA.211 Math For Economics
  • @ Cornell
  • 2026 Spring Linear Algebra for Data Science
  • 2024 Spring / 2025 Spring / 2026 Spring MATH 6220 Applied Functional Analysis
  • 2024 Fall MATH 4250 Numerical Analysis and Differential Equations
  • 2023 Fall MATH 4250 Numerical Analysis and Differential Equations

MENTORSHIP

  • Undergraduate Students
  • Stuti Saria (NYU, Econ & Math Major)
  • Srinath Mahankali (Stuyvesant High School, now at MIT)
  • Carol Long (NYU, then PhD student at Harvard SEAS)
  • Han Liu (NYU, Math major, then M.S. student at Rice University)
  • Baoye Chen (NYU, then M.S. at Columbia, now at Deutsche Bank in New York City)
  • Linglai Chen (NYU, now M.S. student at Harvard)
  • Bowen Zhu (NYU, now M.S. student at Harvard)
  • Wanzhou Lei (NYU, now M.S. student at Harvard)
  • Liu Zhang (Yale-NUS, now Ph.D. student at Princeton)
  • Aparna Gupte (MIT, CS major)
  • Master Students
  • Nicolai Gerber (University of Bonn, 2022 - 2023)
  • Ph.D. Students
  • Jonah Botvinick-Greenhouse (Cornell, 2021 - present)
  • Maria Oprea (Cornell, 2023 - present)
  • Matej Neumann (Cornell, 2023 - present)
  • Nicolas Guerra (Cornell, 2024 - present)
  • Michael Sun (Cornell, 2025 - present)
  • Early-Career Mentees
  • Nicholas Nelsen (Cornell Klarman Fellow, 2025 - present)

NEWS

  • 2026: Serving as Associate Editor of Computational Methods in Applied Mathematics (CMAM), 2026-2031.
  • 2025: Received the Outstanding Youth Academic Award at the 14th Conference on Inverse Problems, Imaging and Applications (Tianjin, China).
  • 12/2025: Co-organized the NeurIPS 2025 Workshop on Structured Probabilistic Inference & Generative Modeling (San Diego, USA).
  • 7/2025: Co-organized IPAM workshop "Sampling, Inference, and Data-Driven Physical Modeling in Scientific Machine Learning" (UCLA, Los Angeles).
  • 4/2025: Co-organized Frontiers in Computational Mathematics, a conference in honor of Bjorn Engquist's 80th birthday (UT Austin, Texas).
  • 4/2025: Co-organized Oberwolfach workshop "Computational Multiscale Methods" (Oberwolfach, Germany).
  • 5/2024: Awarded NSF grant DMS-2409855 (2024-2027), "Metric-Dependent Strategies for Inverse Problem Analysis and Computation".
  • 5/2024: Presented the mini-tutorial "Computational Optimal Transport in Imaging Science" at SIAM Conference on Imaging Science 2024, Atlanta, GA, together with Matthew Thorpe. Here are the tutorial slides Part 1 and Part 2.
  • 4/2024: Co-organized workshop "Women in Optimal Transport" (The Kantorovich Initiative, Vancouver, Canada).
  • 12/2023: Awarded ONR grant ONR-N00014-24-1-2088 (2024-2028), "Optimal Transport Based Strategies in Waves and Dynamics".
  • 7-8/2023: Co-organized BIRS workshop "Applied and Computational Differential Geometry and Geometric PDEs" (Banff, Canada).
  • 6/2023: Co-organized workshop "Emerging Topics in Applications of Optimal Transport" at the Institute for Theoretical Studies, ETH Zurich (Switzerland).
  • 5/2023: External co-organizer of the Frontiers in Applied and Computational Mathematics (FACM) Conference (NJIT, New Jersey, USA).
  • 1/2023: Co-organized the MSRI Special Session on Summer Research in Mathematics (SRiM): Applied and Computational Mathematics at the Joint Mathematics Meetings (Boston, USA).
Yunan Yang, updated February 11, 2026