Yanfang Liu

Assistant Professor

Yanfang Liu
Office Hours

M/T 2:00pm-4:00 pm via Zoom or make an appointment

Degree Information

  • PHD, Michigan Tech University (2021)
  • MS, Michigan Tech University (2019)
  • BS, Chongqing University (2015)

Areas of Expertise

  • Machine Learning for scientific data analysis
  • Inverse problems and their applications
  • Bayesian inference for scientific problems

Biography

  • Assistant Professor, Middle Tennessee State University, Murfreesboro, TN, Aug 2024 – Present
  • Postdoctoral Research Associate, Oak Ridge National Laboratory, Knoxville, TN, Jun 2023 – Jul 2024
  • Visiting Assistant Professor, George Washington University, Washington, DC, Sep 2021 – May 2023

Publications

1. Liu, Yanfang, Yuan Chen, Dongbin Xiu, and Guannan Zhang. A training-free conditional diffusion model for learning stochastic dynamical systems. arXiv preprint arXiv:2410.03108, 2024. https://doi.org/10.48550/arXiv.2410.03108
2. Dan Lu, Liu, Yanfang, Zezhong Zhang, Feng Bao, and Guannan Zhang. A diffusion-based uncertainty quantification method to advance e3sm land model calibration. JGR: Machine Learning and Computation, 1(3):e2024JH000234, 2024. https://doi.org/10.1029/2024JH000234

Read More »

1. Liu, Yanfang, Yuan Chen, Dongbin Xiu, and Guannan Zhang. A training-free conditional diffusion model for learning stochastic dynamical systems. arXiv preprint arXiv:2410.03108, 2024. https://doi.org/10.48550/arXiv.2410.03108
2. Dan Lu, Liu, Yanfang, Zezhong Zhang, Feng Bao, and Guannan Zhang. A diffusion-based uncertainty quantification method to advance e3sm land model calibration. JGR: Machine Learning and Computation, 1(3):e2024JH000234, 2024. https://doi.org/10.1029/2024JH000234
3. Yanfang Liu, Minglei Yang, Zezhong Zhang, Feng Bao, Yanzhao Cao, and Guannan Zhang. Diffusion-model-assisted supervised learning of generative models for density estimation. JMLMC, 5(1), 2024. https://doi.org/10.1615/jmachlearnmodelcomput.2024051346
4. Yanfang Liu, Zhizhang Wu, Jiguang Sun, and Zhiwen Zhang. Deterministic-statistical approach for an inverse acoustic source problem using multiple-frequency limited-aperture data. Inverse Probl. Imaging, 2023. https://www.aimsciences.org//article/doi/10.3934/ipi.2023018
5. Juan Liu, Yanfang Liu, and Jiguang Sun. Reconstruction of modified transmission eigenvalues using Cauchy data. J. Inverse Ill. Posed Probl., (0), 2023. https://doi.org/10.1515/jiip-2022-00141
6. Hang Du, Zhaoxing Li, Juan Liu, Yanfang Liu, and Jiguang Sun. Divide-and-conquer DNN approach for the inverse point source problem using a few single frequency measurements. Inverse Problems, 39(11):115006, 2023. https://iopscience.iop.org/article/10.1088/1361-6420/acfd57
7. Yanfang Liu and Jiguang Sun. Bayesian inversion for an inverse spectral problem of transmission eigenvalues. Res. Math. Sci., 8(3):1–15, 2021. https://link.springer.com/article/10.1007/s40687-021-00288-x
8. Yanfang Liu, Yukun Guo, and Jiguang Sun. A deterministic-statistical approach to reconstruct moving sources using sparse partial data. Inverse Problems, 37(6):065005, 2021. https://iopscience.iop.org/article/10.1088/1361-6420/abf813
9. Zhaoxing Li, Yanfang Liu, Jiguang Sun, and Liwei Xu. Quality-bayesian approach to inverse acoustic source problems with partial data. SIAM J. Sci. Comput., 43(2):A1062–A1080, 2021. https://epubs.siam.org/doi/abs/10.1137/20M132345X
10. Juan Liu, Yanfang Liu, and Jiguang Sun. An inverse medium problem using Stekloff eigenvalues and a Bayesian approach. Inverse Problems, 35(9):094004, 2019. https://iopscience.iop.org/article/10.1088/1361-6420/ab1be9

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Courses

MTSU

  • 2025 Fall: Data 6300 – Data Understanding, Data 6500 – Case Study in Data Science
  • 2025 Summer: Math 1730 – Pre-Calculus
  • 2025 Spring: Data 6300 – Data Understanding, Data 6900 – Topics Seminar in Data Science
  • 2024 Fall: Data 6300 – Data Understanding, Data 6500 – Case Study in Data Science