Dr. Jia “Peter” Liu
Associate Professor in Industrial and Systems Engineering
Biography
Jia “Peter” Liu is an Associate Professor and the Trey Lauderdale Industrial and Systems Engineering Faculty Fellow at the University of Florida. His research interests include statistical learning, deep learning, and AI with applications in advanced manufacturing. He focuses on integrating physics knowledge and data-driven machine learning to enhance the understanding of additive manufacturing (particularly process optimization and fatigue performance of laser powder bed fusion). He also works on smart manufacturing and secure manufacturing, with applications in the aerospace, defense, and automotive sectors.
His research has been funded by NSF, DoD, FAA, and NIST. He has published over 50 journal and conference papers and has been honored with several awards, including the 2024 ASME Rising Star of Mechanical Engineering and the 2023 NSF CAREER Award. He is a senior member of INFORMS, a member of IISE, IEEE, ASME, and SME. He holds a Ph.D. in Industrial and Systems Engineering, an M.S. in Statistics from Virginia Tech, and a B.S. and M.S. in Electrical Engineering from Zhejiang University, China.
Research/teaching interests:
Statistical modeling, machine learning, and data analytics for additive manufacturing.
In-situ sensing, ex-situ inspection, materials characterization, and reliability analysis for metal additive manufacturing.
Privacy-preserving information sharing and large-scale multimodal modeling for manufacturing.
Publications:
1. Li, A., Liu, J. (2026), “FedCOT: Personalized Federated Transfer Learning with Conditional Optimal Transport for Manufacturing Predictive Modeling”, IEEE Transactions on Automation Science and Engineering, 23, 6363-6382. 10.1109/TASE.2026.3673201.
2. Li, A., Poudel, A., Shao, S., Shamsaei, N., Liu, J. (2025), “Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework”, IISE Transactions, 57(11), 1344–1359. https://doi.org/10.1080/24725854.2024.2397383.
3. Mahmood, S., Baugh, L., Lee, S., Ahmad, N., Silva, D., Vinel, A., Liu, J., Shao, S., Shamsaei, N., Jackson, R., Schulze, K. (2025), “A comparative analysis of non-destructive surface topography measurement techniques for additively manufactured metal parts”, Additive Manufacturing, 105, 104791, DOI: 10.1016/j.addma.2025.104791.
4. Ahmad, N., Irfan, S.*, Maleki, E., Lee, S., Liu, J., Shao, S., Shamsaei, S. (2025), “Determining critical surface features affecting fatigue behavior of additively manufactured Ti-6Al-4V”, International Journal of Fatigue, 197, 108956, DOI: 10.1016/j.ijfatigue.2025.108956.
5. Liu, J., Ye, J., Silva, D., Vinel, A., Shamsaei, N., Shao, S. (2023), “A Review of Machine Learning Techniques for Process and Performance Optimization in Laser Beam Powder Bed Fusion Additive Manufacturing”, Journal of Intelligent Manufacturing, 34 (8), 3249-3275.
6. Poudel, A., Yasin, M., Ye, J., Liu, J., Vinel, A., Shao, S., Shamsaei, N. (2022), “Feature-based Volumetric Defect Classification in Metal Additive Manufacturing”, Nature Communications, 13 (1), 6369.
7. Li, A., Baig, S., Liu, J., Shao, S., Shamsaei, N. (2022), “Defect Criticality Analysis on Fatigue Life of L-PBF 17-4 PH Stainless Steel via Machine Learning”, International Journal of Fatigue, 163, 107018.
Notable awards:
NSF Faculty Early Career Development Program (CAREER) Award, 2023
ASME Rising Star of Mechanical Engineering, 2024
NIST Additive Manufacturing Benchmark Test Series (AM Bench 2025 Challenges) Winner, 2025
Trey Lauderdale Industrial and Systems Engineering Faculty Fellow, 2025-2028