I am an Assistant Professor in the Department of Mathematical Sciences at Middle Tennessee State University. My research is in computational mathematics, with a focus on inverse problems, Bayesian inference, uncertainty quantification, scientific machine learning, and data-driven methods for complex scientific systems. Before joining MTSU, I was a Postdoctoral Research Associate at Oak Ridge National Laboratory from 2023 to 2024 and a Visiting Assistant Professor at George Washington University from 2021 to 2023. I received my Ph.D. in 2021 and M.S. in 2019 from Michigan Technological University, and my B.S. in 2015 from Chongqing University.

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1.     Yanfang Liu, Chen Yuan, Dongbin Xiu, and Guannan Zhang. A training-free conditional diffusion model for learning stochastic dynamical systems. SIAM Journal on Scientific Computing, 2025. https://doi.org/10.1137/24M1699589

2.     Minglei Yang, Yanfang Liu, Diego Del-Castillo-Negrete, Yanzhao Cao, and Guannan Zhang. Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains. Journal of Computational Physics, 2025. https://doi.org/10.1016/j.jcp.2025.114434

3.     Ming Fan, Yanfang Liu, Dan Lu, Hongsheng Wang, and Guannan Zhang. A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage. Journal of Hydrology, 2025. https://doi.org/10.1016/j.jhydrol.2024.132323

4.     Yanfang Liu, Alisa Bryantseva, Miroslav Stoyanov, Feng Bao, and Guannan Zhang. Diffusion-based sparse-grid generative models for density estimation. Applied Mathematics for Modern Challenges, 2024. https://doi.org/10.3934/ammc.2024019

5.     Dan Lu, Yanfang Liu, 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

6.     Yanfang Liu, Minglei Yang, Zezhong Zhang, Feng Bao, Yanzhao Cao, and Guannan Zhang. Diffusion-model-assisted supervised learning of generative models for density estimation. Journal of Machine Learning for Modeling and Computing, 5(1), 2024. https://doi.org/10.1615/jmachlearnmodelcomput.2024051346

7.     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 Problems and Imaging, 2023. https://www.aimsciences.org//article/doi/10.3934/ipi.2023018

8.     Juan Liu, Yanfang Liu, and Jiguang Sun. Reconstruction of modified transmission eigenvalues using Cauchy data. Journal of Inverse and Ill-posed Problems, 2023. https://doi.org/10.1515/jiip-2022-0014

9.     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

10.   Yanfang Liu and Jiguang Sun. Bayesian inversion for an inverse spectral problem of transmission eigenvalues. Research in the Mathematical Sciences, 8(3):1–15, 2021. https://link.springer.com/article/10.1007/s40687-021-00288-x

11.   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

12.   Zhaoxing Li, Yanfang Liu, Jiguang Sun, and Liwei Xu. Quality-Bayesian approach to inverse acoustic source problems with partial data. SIAM Journal on Scientific Computing, 43(2):A1062–A1080, 2021. https://epubs.siam.org/doi/abs/10.1137/20M132345X

13.   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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•     Training-free Conditional Diffusion Model for Stochastic Dynamical Systems Learning. Talk, SIAM Conference on Uncertainty Quantification (UQ26), Minneapolis, MN, Mar. 2026.

•     Training-free Conditional Diffusion Model for Stochastic Dynamical Systems Learning. Talk, SIAM Conference on Computational Science and Engineering (CSE25), Fort Worth, TX, Mar. 2025.

•     Training-free Conditional Diffusion Model for Stochastic Dynamical Systems Learning. Poster, SIAM Conference on Mathematics of Data Science (MDS24), Atlanta, GA, Oct. 2024.

•     Training-free Generative Diffusion Model for Density Estimation. Invited talk, Applied Math Seminar, George Washington University, Oct. 2024.

•     Machine Learning for Data-Driven Forward and Inverse Uncertainty Quantification. Invited talk, Middle Tennessee State University, Mar. 2024.

•     Hybrid Approaches for Inverse Source Problems Using Limited Data. Invited talk, Oak Ridge National Laboratory, Apr. 2023.

•     Deterministic-Statistical Approach for an Inverse Acoustic Source Problem using Multiple-Frequency Limited-Aperture Data. Talk, SIAM Southeastern Atlantic Section Annual Meeting, Virginia Tech, Mar. 2023.

•     Deterministic-Statistical Approach for an Inverse Acoustic Source Problem using Multiple-Frequency Limited-Aperture Data. Poster, Women in Scientific Computing on Complex Physical and Biological Systems, Gainesville, FL, Oct. 2022.

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MTSU

• 2026 Fall: Math 1910 – Calculus I; Data 6990 – Topics Seminar in Data Science; Data 6700 – Independent Study Data Science; 
• 2026 Summer: Data 6310 – Data Exploration; Data 6700 – Independent Study Data Science
• 2025 Fall: Data 6300 – Data Understanding, Data 6500 – Case Study in Data Science
• 2025 Summer: Math 1730 – Pre-Calculus; Data 2025 – Communicating in Data 
• 2025 Spring: Data 6300 – Data Understanding, Data 6990 – Topics Seminar in Data Science
• 2024 Fall: Data 6300 – Data Understanding, Data 6500 – Case Study in Data Science

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