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Navid Zobeiry

Faculty Photo

Assistant Professor
Materials Science & Engineering

Pronouns: he/him


Dr. Navid Zobeiry is an Assistant Professor of Materials Science & Engineering at the University of Washington. His research primarily explores the convergence of materials science, data science, and advanced manufacturing technologies. In close collaboration with aerospace manufacturers and materials suppliers, Zobeiry's research currently focuses on three key areas: 1) Smart testing methods using theory-guided machine learning alongside traditional characterization techniques, 2) Smart manufacturing methods, leveraging automation, sensing, model-based engineering, and machine learning, and 3) Smart engineering methods for accelerated aerospace part and process certification and qualification, aided by machine learning and physics-based simulations. Central to these research areas is a novel machine learning framework that is both probabilistic and deterministic, seamlessly integrating model-based engineering data with targeted testing data and underlying physical laws. Employing this innovative approach, his research group has developed several patented AI software solutions specifically for the aerospace sector, with notable aerospace entities acquiring exclusive licenses for some of these innovations.


  • University of British Columbia
  • University of British Columbia
  • University of Tehran

Previous appointments

  • Research Associate and Lecturer, University of British Columbia

Select publications

  1. Schoenholz, C., & Zobeiry, N. (2024). An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning. Composites Part A: Applied Science and Manufacturing, 176, 107842.
  2. Schoenholz, C., Li, S., Bainbridge, K., Huynh, V., Gray, A., & Zobeiry, N. (2023). Accelerated In Situ Inspection of Release Coating and Tool Surface Condition in Composites Manufacturing Using Global Mapping, Sparse Sensing, and Machine Learning. Journal of Manufacturing and Materials Processing, 7(3), 81.
  3. Byar, A. D., Kabir, M. H., Stere, A. I., Doty, C., Joseph, A., & Zobeiry, N. (2023). Simulation Model Validation for Structure Material Characterization. U.S. Patent Application No. 17/648,526.
  4. Kabir, M. H., Byar, A. D., Stere, A. I., DePauw, T. C., Doty, C. M., Joseph, A. P. K., & Zobeiry, N. (2023). Outlier detection and management. U.S. Patent Application No. 17/654,087.
  5. Wynn, M., & Zobeiry, N. (2022). Investigating the Effect of Temperature History on Crystal Morphology of Thermoplastic Composites Using In Situ Polarized Light Microscopy and Probabilistic Machine Learning. Polymers, 15(1), 18.
  6. Freed, Y., Salviato, M., & Zobeiry, N. (2022). Implementation of a probabilistic machine learning strategy for failure predictions of adhesively bonded joints using cohesive zone modeling. International Journal of Adhesion and Adhesives, 118, 103226.
  7. Lee, A., Wynn, M., Quigley, L., Salviato, M., & Zobeiry, N. (2022). Effect of temperature history during additive manufacturing on crystalline morphology of PEEK. Advances in Industrial and Manufacturing Engineering, 4, 100085.
  8. Humfeld, K. D., Gu, D., Butler, G. A., Nelson, K., & Zobeiry, N. (2021). A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control. Composites Part B: Engineering, 223, 109150.
  9. Reiner, J., Vaziri, R., & Zobeiry, N. (2021). Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Composite Structures, 273, 114290.
  10. Zobeiry, N., & Humfeld, K. D. (2021). A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Engineering Applications of Artificial Intelligence, 101, 104232.
  11. Zobeiry, N., Lee, A., & Mobuchon, C. (2020). Fabrication of transparent advanced composites. Composites Science and Technology, 197, 108281.
  12. Zobeiry, N., Reiner, J., & Vaziri, R. (2020). Theory-guided machine learning for damage characterization of composites. Composite Structures, 246, 112407.

Honors & awards

  • Certificate of Appreciation at the Boeing Distinguished Researcher and Scholar Seminar Series (B-DRASS), Boeing Company, Seattle, 2019
  • Teaching Faculty of the Year Award by a vote of the junior class, Materials Science and Engineering Department, University of Washington, 2020