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Sen Liu, Ph.D

Assistant Professor

Postdoc, Stanford University, SLAC National Accelerator Laboratory, 2023
Ph.D. Mechanical Engineering, Colorado School of Mines, 2021

Contact Information:

Rougeou Hall, Rm 225
Phone: 337-482-1207


Courses Taught:

MCHE 374 – Mechanical Systems Analysis (TBA)
MCHE 474G - Control Systems

Research Interests:

  • Laser Additive Manufacturing
  • In-situ Monitoring and Process Control
  • Machine Learning
  • Computer Vision
  • AI-powered Materials Design

Honors and Awards:

  • Outstanding Ph.D. Dissertation Award
  • Dr. Bhakta Rath Research Award
  • ASME ISPS Division Graduate Student Scholarship, Santa Clara, CA=
  • IEEE International Conference on Magnetics, Student Travel Award ($1000), Beijing

Selected Publications:

S. Liu, B. Amin-Ahmadi, R. Liu, Q. Zheng, X. Zhang, Automated Phase Segmentation and Quantification of High-resolution TEM Image for Alloy Design, Materials Characterization, 2023, 199: 112779.

S. Liu, A. P. Stebner, B. B. Kappes, X. Zhang, Machine Learning for Knowledge Transfer Across Multiple Metals Additive Manufacturing Printers, Additive Manufacturing, 2021, 39: 101877.

S. Liu, B. B. Kappes, B. Amin-ahmadi, O. Benafan, X. Zhang, A. P. Stebner, Physics-informed Machine Learning for Composition – Process – Property Design: Shape Memory Alloy Demonstration, Applied Materials Today 22: 100898. 

N. Jamnikar, S. Liu (co-first), C. Brice, X. Zhang, In situ Microstructure Property Prediction by Modeling Molten pool-quality Relations for Wire-feed Laser Additive Manufacturing, Journal of Manufacturing Processes, 79: 803-814.

S. Gorgannejad, A.A. Martin, J.W. Nicolino, M. Strantza, G.M. Guss, S. Khairallah, J.B. Forien, V. Thampy, S. Liu, P. Quan, C.J. Tassone, N.P. Calta (2023) Localized Keyhole Pore Prediction during Laser Powder Bed Fusion via Multimodal Process Monitoring and X-ray Radiography, Additive Manufacturing, 2023.

S. Liu, C. Brice, X. Zhang, Hierarchical Bead Materials Multi-property Design for Wire-feed Laser Additive Manufacturing, Journal of Manufacturing Processes, 2022, 80: 546-557.