Amir Kordijazi
Assistant Professor of Industrial Engineering
Education
- PhD (Industrial & Manufacturing Engineering), 2021
- MSc (Materials Science & Engineering), 2012
- BSc (Materials Science & Engineering), 2009
Research Interests
Dr. Kordijazi’s current research interests include:
- Computer vision for industrial applications
- Predictive analytics for advanced manufacturing processes
- Autonomous experimentation
His research has broad applicability across diverse industry sectors, spanning biomedical, marine, aerospace, and semiconductor industries, among others.
Dr. Kordijazi is an interdisciplinary scholar with a strong passion for teaching and research, specializing in the field of Industry 4.0. His primary focus lies in the application of artificial intelligence (AI) and data analytics in industrial contexts. In 2021, he earned his Ph.D. in Industrial & Manufacturing Engineering from the University of Wisconsin, Milwaukee. During his doctoral studies, he dedicated his research to developing and employing AI/ML and statistical techniques, aimed at improving the design, characterization, and manufacturing processes of high-performance alloys and nanocomposites. Following his Ph.D., he pursued a postdoctoral position at the University at Albany – SUNY. In this phase, his work was centered on the advancement of cutting-edge computer vision algorithms for characterizing and performing metrology on the latest generations of nanoscale electronic materials, utilizing four-dimensional scanning electron microscopy data.
Dr. Kordijazi has published over 35 peer-reviewed articles published in renowned journals such as Surfaces and Interfaces, Tribology International, Langmuir, and Materials Today Communications. His academic achievements include receiving the Graduate Student Excellence Fellowship and Chancellor’s Graduate Student Awards from the University of Wisconsin, Milwaukee, as well as earning two Best Paper Awards from the American Foundry Society.
Selected Publications
- 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
- Zhao, J., Kordijazi, A., Valensa, C., Roshan, H., Kolhe, Y., & Rohatgi, P. (2022). Behavior of Steel Foam Sandwich Members Cast with 3D Printed Sand Cores, JOM, 74 (5), 2083–2093.
- Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2022). Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites. Tribology International, 165, 107326.
- 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.
- Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2021). Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribology International, 161, 107065.
- 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.
- Kordijazi, A., Weiss, D., Das, S., Behera, S., Roshan, H. M., & Rohatgi, P. (2020). Effect of solidification time on microstructure, wettability, and corrosion properties of A205-T7 aluminum alloys. International Journal of Metalcasting, 15(1), 2-12.
- Kordijazi, A., Behera, S. K., Suri, S., Wang, Z., Povolo, M., Salowitz, N., & Rohatgi, P. (2020). Data-driven modeling of wetting angle and corrosion resistance of hypereutectic cast Aluminum-Silicon alloys based on physical and chemical properties of surface. Surfaces and Interfaces, 20, 100549.
Education
- PhD (Industrial & Manufacturing Engineering), 2021
- MSc (Materials Science & Engineering), 2012
- BSc (Materials Science & Engineering), 2009
Research Interests
Dr. Kordijazi’s current research interests include:
- Computer vision for industrial applications
- Predictive analytics for advanced manufacturing processes
- Autonomous experimentation
His research has broad applicability across diverse industry sectors, spanning biomedical, marine, aerospace, and semiconductor industries, among others.