Title

Minliang Liu

Headshot Placeholder
Minliang Liu
Title/Position
Assistant Professor
Areas of Research

Areas of Research

Cardiovascular Engineering
Contact

Contact

WMRB 5335Emory
minliang.liu@emory.edu
404.727.1393
Education

Education

  • Ph.D. in Mechanical Engineering, Georgia Institute of Technology, 2020
  • B.Eng. in Mechatronic Engineering, Zhejiang University, 2015
Research Interests

Research Interests

Dr. Liu’s research lies at the intersection of cardiovascular biomechanics, computational modeling, digital twins, and scientific machine learning. The goal of his research is to advance the prognosis and treatment of cardiovascular diseases through patient-specific digital twins informed by clinical and experimental data, with a particular focus on thoracic aortic and valvular diseases. His approach encompasses forward and inverse computational mechanics using differentiable solvers, data-driven and physics-informed machine learning, fluid-structure interaction analysis, constitutive and failure modeling of cardiovascular tissues, and experimental tissue mechanics.

Teaching Interests

Teaching Interests

Dr. Liu’s teaching interests include biomechanics, computational methods, and scientific machine learning, with an emphasis on applications in cardiovascular mechanics.
Publications

Publications

1. Y. Du, Y. Wu, H. L. Cebull, B. Liao, R. Agarwal, A. Meraz, H. Dong, A. Kalyanasundaram, J. N. Oshinski, R. L. Gleason, J. A. Elefteriades, B. G. Leshnower, and M. Liu, “Structural Stress as a Predictor of the Rate and Spatial Location of Aortic Growth in Uncomplicated Type B Aortic Dissection,” IEEE Transactions on Biomedical Engineering, 2026.
2. Z. Li, D. H. Pak, J. S. Duncan, L. Liang, and M. Liu, “An Integrated DNN-FEA Approach for Inverse Identification of Passive, Heterogeneous Material Parameters of Left Ventricular Myocardium,” Computer Modeling in Engineering & Sciences, 146(1), 9, 2026.
3. H. Tanoto, Z. Zhou, K. Chen, R. Qiu, H. Fan, J. Z. Chen, E. Milton, Y. Zhou, and M. Liu, “Predicting biaxial failure strengths of aortic tissues using a dispersed fiber failure model,” Extreme Mechanics Letters, 75, 102287, 2025.
4. M. Liu, L. Liang, H. Dong, W. Sun, and R. L. Gleason, “Constructing growth evolution laws of arteries via reinforcement learning,” Journal of the Mechanics and Physics of Solids, 168, 105044, 2022.
5. M. Liu, L. Liang, Q. Zou, Y. Ismail, X. Lou, G. Iannucci, E. P. Chen, B. G. Leshnower, J. A. Elefteriades, and W. Sun, “A probabilistic and anisotropic failure metric for ascending thoracic aortic aneurysm risk assessment,” Journal of the Mechanics and Physics of Solids, 155, 104539, 2021.
6. M. Liu, L. Liang, and W. Sun, “A generic physics-informed neural network-based constitutive model for soft biological tissues,” Computer Methods in Applied Mechanics and Engineering, 372, 113402, 2020.