入选国家高层次青年人才计划，中国声学学会检测分会副主任、中国仪器仪表学会声学仪器分会副主任、中国仪器仪表学会地学仪器分会理事，承担和参与国家重点研发项目、军科委重大专项、中组部人才计划等项目。Instrumentation期刊编委，仪器仪表学报专刊主编，担任国际超声大会、国际定量无损检测年会、中国声学大会等声学相关会议主席、分会主席。

教育背景

1. 2010-2014 宾夕法尼亚州立大学，工程科学与力学，博士
2. 2007-2010 浙江大学，机械制造及自动化，硕士
3. 2003-2007 重庆大学，机械设计制造及其自动化，学士

工作经历

1. 2020-至今 天津大学，精密仪器与光电子工程学院，教授
2. 2018-2020 怀俄明大学，机械工程，助理教授
3. 2014-2018 斯伦贝谢道尔研究所，研究员
4. 2014-2014 宾夕法尼亚州立大学，工程科学与力学，博士后

　　主要从事复杂结构介质声场传播和散射理论、智能反演成像方法、传感器与仪器系统开发研究。

部分专利

1. 1. 刘洋，赵春雨，李健，曾周末. 一种基于超声导波的适用于任意形状截面的应力检测方法. 天津市：CN114739546B, 2022.
2. 2. 刘洋，王筱岑，童君开，李健，曾周末. 一种基于超声导波和卷积神经网络的腐蚀成像方法. 天津市：CN113848252B, 2022.
3. 3. 李健，赵成威，刘洋. 一种适用于检测材料早期疲劳损伤的新成像检测方法. 天津市：CN113008992B, 2021.
4. 4. Y. Liu, R. D'Angelo, S. Zeroug, et al. Guided mode beamforming for probing open-hole and cased-hole well environments. US11531132B2, 2022.
5. 5. Y. Liu, K B. Sinha, S. Zeroug. Methods for characterizing multi-string cased wells using wide frequency bandwidth signals. US11131182B2, 2021.
6. 6. E. Khajeh, Y. Liu. Formation measurements using nonlinear guided waves, US10094945B2. 2018.
7. 7. E. Khajeh, Y. Liu. Formation measurements using flexural modes of guided waves. US9389330B2, 2016.

近年部分国际会议大会及特邀报告

1. 2023年，IEEE第16届国际电子测量与仪器学术会议，Ultrasonics, Sensors and AI: Applications in Quantitative Ultrasonic Guided Wave NDE of Critical Structures，大会报告，中国，哈尔滨
2. 2023年，远东无损检测新技术论坛，超声导波智能定量化成像技术研究，特邀报告，中国，天津
3. 2022年，第16届压电和声波理论及器件应用研讨会，超声导波定量化成像方法研究，特邀报告中国，南京
4. 2022年，ASME国际机械工程大会，Stress Inversion for Acoustoelastic Guided Waves with Arbitrary Section, 特邀报告，美国，哥伦布 （俄亥俄州）
5. 2022年，第49届国际定量无损检测大会，Compressive Sensing and Deep Learning Enhanced Imaging Algorithm for Sparse Guided Wave Array，特邀报告，美国，圣地亚哥（加州）
6. 2021年，中国声学大会，多尺度超声成像，特邀报告，中国，上海

近期代表性论文

1. 1. L. Wang, J. Li, S. Chen, Z. Fan, Z. Zeng, Y. Liu*, “Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield”, J. Acoust. Soc. Am. 155(3), 2024
2. 2. J. Ren, J. Li, C. Li, S. Chen, L. Liang, Y. Liu* “Deep learning with physics-embedded neural network for full waveform ultrasonic brain imaging”, IEEE Transactions on Medical Imaging, 2024.
3. 3. H. Wang, J. Li, L. Wang, L. Liang, Z. Zeng, Y. Liu*, “On acoustic fields of complex scatters based on physics-informed neural networks”, Ultrasonics, 2023, 128, 106872.
4. 4. L. Wang, H. Wang, L. Liang, J Li, Z. Zeng, Y. Liu*, “Physics-informed neural networks for transcranial ultrasound wave propagation”, Ultrasonics, 2023, 132, 107026.
5. 5. L. Lv, S. Chen, J. Tong, X. Chen, Z. Zeng, Y. Liu*, “Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network”, Measurement Science and Technology, 2023, 34 (11), 115401.
6. 6. S. Zhang, Z. Zeng, X. Wang, S. Chen, Y. Liu*, “Imaging and characterization of cement annulus and bonding interfaces in cased wells with fully connected neural network”, Geophysics, 2023, 88(6): D357-D369.
7. 7. W. Zhou, S. Wang, Q. Wu, X. Xu, X. Huang, G. Huang, Y. Liu*, Z. Fan*, “An inverse design paradigm of multi-functional elastic metasurface via data-driven machine learning”, Materials & Design 226, 111560.
8. 8. D. Wang, X. Wang, S. Chen, J. Li, L. Liang, Y. Liu*, “Joint Learning of Sparse and Limited-View Guided Waves Signals for Feature Reconstruction and Imaging”, Ultrasonics, 2023, 137, 107200.
9. 9. X. Wang, J. Li, D. Wang, X. Huang, L. Liang, Z. Tang, Y. Liu*, “Sparse ultrasonic guided wave imaging with compressive sensing and deep learning”, Mechanical Systems and Signal Processing, 2022, 178: 109346.
10. 10. C. Zhao, X. Chen, J. Li, Y. Liu*. “Stress inversion in waveguides with arbitrary cross sections with acoustoelastic guided waves”, Journal of Applied Physics, 2022, 131(24).
11. 11. X. Wang, M, Lin, J. Li, J. Tong, X. Huang, L. Liang, Y. Liu*, “Ultrasonic guided wave imaging with deep learning: Applications in corrosion mapping”, Mechanical Systems and Signal Processing, 2022, 169: 108761.
12. 12. C. Zhao, J. Li, M. Lin, X. Chen, Y. Liu*, “Ultrasonic guided wave inversion based on deep learning restoration for fingerprint recognition”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022, 69(10): 2965-2974.
13. 13. J. Tong, X. Wang, J. Ren, M. Lin, J. Li, H. Sun, Y. Liu*, “Transcranial Ultrasound Imaging With Decomposition Descent Learning-Based Full Waveform Inversion”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022, 69(12): 3297-3307.
14. 14. J. Tong, M. Lin, X. Wang, J. Li, J. Ren, L. Liang, Y. Liu*, “Deep learning inversion with supervision: A rapid and cascaded imaging technique”, Ultrasonics, 2022, 122: 106686.
15. 15. M. Lin, Y. Liu*, “Guided wave tomography based on supervised descent method for quantitative corrosion imaging”, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021, 68(12): 3624-3636.