Navid Zobeiry

Biography

Dr. Navid Zobeiry is an associate professor in the Materials Science and Engineering department at the University of Washington, with an adjunct position in the Aeronautics and Astronautics department. His research focuses on the intersection of materials science, data science, and advanced manufacturing, working in close collaboration with aerospace manufacturers and materials suppliers. Zobeiry’s work addresses three main areas: 1) Smart testing methods that integrate physics-informed machine learning with traditional characterization techniques; 2) Smart manufacturing methods that leverage automation, sensing, model-based engineering, and machine learning; and 3) Smart engineering methods that focus on uncertainty quantification to accelerate aerospace part and process certification and qualification, using machine learning and physics-based simulations. A central feature of his research is a novel machine learning framework that combines probabilistic and deterministic approaches, seamlessly integrating model-based engineering data, targeted testing, and physical laws. This innovative approach has led to the development of several patented AI software solutions, with some being exclusively licensed to notable aerospace companies.

Education

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

Previous appointments

• Assistant Professor, University of Washington
• Research Associate and Lecturer, University of British Columbia

Select publications

1. Eskandariyun A., Fu H., Zobeiry N. (2026). An Integrated Process–Failure Simulation Framework for Predicting Composite Allowables via Multi-Fidelity Stochastic Simulation and Machine Learning, Composites Part B: Engineering, 326:114051.
2. Fu H., Eskandariyun A., Portales P., Johnson K., Morton A., Wynn M., Zobeiry N. (2026). Artificial Intelligence and Machine Learning in Composite Materials: A Comprehensive Literature Review and Bibliometric Analysis, Composites Part A: Applied Science and Manufacturing, 210:110092.
3. Schoenholz C., Fu H., Cheng R., Zobeiry N. (2026). Uncertainty-Aware Accelerated Characterization of Process-Dependent Composite Properties Using Probabilistic Machine Learning, Composites Part A: Applied Science and Manufacturing, 210:110039.
4. Morton A., Zobeiry N. (2026). Machine learning–statistical inference of prepreg conditioning history from surface morphology, Composites Science and Technology, 275:111494.
5. Fu H., Zobeiry N. (2026). Data-driven Machine Learning Meta-Analysis of Process–Property Relationships in Polymer Additive Manufacturing: A Case Study on FFF-Printed PEEK, Journal of Manufacturing Processes, 163:100-113.
6. Portales P., Gray A., Zobeiry N. (2025). Efficient characterization and optimization of pyrolysis in carbon-carbon composites through machine learning, Composites Part A: Applied Science and Manufacturing, 190:108664.
7. Schoenholz, C., Zappino, E., Petrolo, M., Zobeiry, N. (2024). Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning. Composites Part B: Engineering, 280, 111499.
8. Wynn, M., Oster, L., Chase, G., Salviato, M., Zobeiry, N. (2024). Assessment of the effect of processing parameters on peel failure of laser-assisted automated fiber placed thermoplastic composites. Manufacturing Letters, 40, 93-96.
9. Picazo, P. P., Cheng, R., Gray, A., Zobeiry, N. (2024). A Machine Learning-based Accelerated Pyrolysis Characterization and Optimization of High-temperature Composites. SAMPE Journal, 60(2).
10. Schoenholz, C., Zobeiry, N. (2024). Investigating the Impacts of Processing Variability on Tool-part Interaction for Interply-toughened Aerospace Composites using a Novel Shear Technique. Composites Part A: Applied Science and Manufacturing, 178, 107973.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. Reiner, J., Vaziri, R., Zobeiry, N. (2021). Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Composite Structures, 273, 114290.
17. 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.
18. Zobeiry, N., Reiner, J., Vaziri, R. (2020). Theory-guided machine learning for damage characterization of composites. Composite Structures, 246, 112407.

Honors & awards

• Best Paper Award (2026) | Artificial Intelligence for Composite Materials Conference 2026 (AIComp26)
• Outstanding Technical Paper Award – Third Place (2026) | Society for the Advancement of Material and Process Engineering (SAMPE)
• Best Paper Award for 2024 Conference (2025) | American Society for Composites (ASC), USA
• Faculty of the Year Award (2025) | Materials Science and Engineering Department, University of Washington
• Best Paper Award (2022) | Society for the Advancement of Material and Process Engineering (SAMPE) University Research Symposium (URS) competition
• Teaching Faculty of the Year Award (2020) | Materials Science and Engineering Department, University of Washington

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