Amir Kordijazi

Assistant Professor of Industrial Engineering

207-780-5447

amir.kordijazi@maine.edu

135 John Mitchell Center, Gorham Campus

Education

PhD (Industrial & Manufacturing Engineering), 2021
MSc (Materials Science & Engineering), 2012
BSc (Materials Science & Engineering), 2009

Current Courses

EGN 498: Applied Machine Learning
IDE 481: Engineering Statistics
EGN 160: Introduction to Programming: Python

Research Interests

Dr. Kordijazi’s current research interests include:

Self-Driving Laboratories: Autonomous experimentation integrating AI, robotics, and automation for intelligent manufacturing.
Digital Twin Systems: Virtual replicas for simulating, monitoring, and optimizing manufacturing and materials processes.
Predictive Analytics: ML and statistical models to forecast material properties, process outcomes, and equipment health.
Computer Vision: Intelligent visual analysis for materials characterization and industrial inspection.

Department of Engineering College of Science, Technology, & Health

Dr. Amir Kordijazi is an interdisciplinary scholar whose research and teaching bridges artificial intelligence (AI), machine learning (ML), and advanced manufacturing. His work focuses on developing AI-guided digital twin architectures, predictive analytics, and autonomous experimentation to enable the next generation of intelligent, data-driven manufacturing systems.

He earned his Ph.D. in Industrial and Manufacturing Engineering from the University of
Wisconsin–Milwaukee in 2021, where his research centered on applying AI/ML and statistical modeling to enhance the design and manufacturing of high-performance materials. Following his doctoral studies, he completed a postdoctoral fellowship at the University at Albany–SUNY, developing advanced computer vision algorithms for nanoscale material characterization using four-dimensional electron microscopy data.

In Fall 2023, Dr. Kordijazi joined the University of Southern Maine as an Assistant Professor of Industrial Engineering, where he founded and directs the Self-Driving Manufacturing Lab (SDML). His lab integrates robotics, automation, and machine learning to advance autonomous, data-driven process optimization. His research has been supported by federal (DOE), state (Maine Economic Improvement Fund, MEIF; Maine Space Grant Consortium, MSGC), and institutional (MCEC) funding sources. Current work advances machine learning–integrated digital twins for predictive modeling and closed-loop optimization of complex manufacturing processes.

Dr. Kordijazi has published over 40 peer-reviewed articles and has received multiple honors, including the Faculty Senate Award for Scholarship at the University of Southern Maine, the Inaugural Associate Fellowship from the Institute of Medicine at the University of Maine, the Graduate Student Excellence Fellowship, and two Best Paper Awards from the American Foundry Society.

Selected Publications

Zier, S., Hoeft, Z., Quinn, J. P., Kordijazi, A., Howell, C., & Bousfield, D. (2025). Single-step coating of cellulose nanofibrils on paper for sustainable food packaging. Cellulose, 1-19.
Diebold, A. C., Ophus, C., Kordijazi, A., Consiglio, S., Lombardo, S., Triyoso, D., ... & Leusink, G. (2025). Template Matching Approach for Automated Determination of Crystal Phase and Orientation of Grains in 4D-STEM Precession Electron Diffraction Data for Hafnium Zirconium Oxide Ferroelectric Thin Films. Microscopy and Microanalysis, 31(2), ozaf019
Behera, S., Kumar, A., Kordijazi, A., Weiss, D., & Rohatgi, P. K. (2024). Tribological analysis and machine learning modeling of nickel-coated graphite reinforced A206 metal matrix composites. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications
Alrfou, K., Zhao, T. & Kordijazi, A. (2024). Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives, Integrating Materials and Manufacturing Innovation
Kordijazi, A., Behera, S.K., Jamet, A. et al. (2024). Predictive Analysis of Water Wettability and Corrosion Resistance of Secondary AlSi10MnMg(Fe) Alloy Manufactured by Vacuum-Assisted High Pressure Die Casting. International Journal of Metalcasting.
Alrfou, K., Zhao, T. & Kordijazi, A. (2024). CS-UNet: A generalizable and flexible segmentation algorithm. Multimedia Tools and Applications.
Rohatgi, M., & Kordijazi, A. (2023). Application of machine learning to mechanical properties of copper-graphene composites, MRS Communications, 13, 111–116.
Alrfou, K., Kordijazi, A., Rohatgi, P. & Zhao, T. (2022). Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron, Materials Today Communications, 30, 103174
Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2022). Triboinformatics approach for friction and wear prediction of Al-Graphite composites using machine learning methods. Journal of Tribology, 144(1), 011701
Kordijazi, A., Zhao, T., Zhang, J., Alrfou, K., & Rohatgi, P. (2021). A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications. JOM, 73, 2060–2074.
Kordijazi, A., Behera, S., Patel, D., Rohatgi, P., & Nosonovsky, M. (2021). Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling. Langmuir, 37(12), 3766-3777.
Kordijazi, A., Roshan, H. M., Dhingra, A., Povolo, M., Rohatgi, P. K., & Nosonovsky, M. (2020). Machine-learning methods to predict the wetting properties of iron-based composites. Surface Innovations, 9(2–3), 111-119.

Additional Publications

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