Tarek Echekki
Associate Department Head
- Phone: (919) 515-5238
- Email: techekk@ncsu.edu
- Office: Engineering Building III (EB3) 3234
- Website: https://echekki.wordpress.ncsu.edu/
In addition to his duties as Associate Department Head, Dr. Tarek Echekki also serves as MAE’s Director of Undergraduate Programs.
At the graduate level, Dr. Echekki has taught Fluid Dynamics of Combustion I (MAE 504) and the follow up advanced combustion course, Fluid Dynamics of Combustion II (MAE 704). He also has taught the graduate Fluid Dynamics course, Foundations of Fluid Dynamics (MAE 550) and an introduction to Turbulence, Turbulence (MAE 776).
At the undergraduate level, he has taught Engineering Thermodynamics I and II (MAE 201 and MAE 302) and fluid Mechanics I (MAE 308).
Combustion plays an important role in the solution of many of the engineering problems that we face today. Graduate students who work with Dr. Echekki are also drawn to this area because of its breadth. The reliance of combustion on thermodynamics, heat transfer, and fluid mechanics means that the subject is never boring and provides a foundation from which the student can later branch out.
Outside of work, Dr. Echekki spends time with his family and friends.
Publications
- Ultra-stretchable superomniphobic surfaces via machine-learning-guided laser ablation
- Zarei, M. J., Pillai, S., Rather, A. M., Barrubeeah, M. S., Echekki, T., & Kota, A. K. (2026, February 1), Matter. https://doi.org/10.1016/j.matt.2025.102610
- Data-driven modeling and simulation of turbulent combustion
- Echekki, T. (2025, October 16), Physical Review Fluids. https://doi.org/10.1103/2fln-qs74
- ESSCI 2026 Spring Meeting LaTeX Template
- Echekki, T. (2025, November 20), Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17664518
- ESSCI 2026 Spring Meeting LaTeX Template
- Echekki, T. (2025, November 20), Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17664519
- Gravity Effects on Backdraft Phenomena in an Enclosure with Varying Opening Geometries
- Devananda, V. V., & Echekki, T. (2025, July 30), Microgravity Science and Technology, Vol. 37. https://doi.org/10.1007/s12217-025-10199-z
- Physics-constrained machine learning for reduced composition space chemical kinetics
- Kumar, A., & Echekki, T. (2025, January 1), Data-Centric Engineering, Vol. 6. https://doi.org/10.1017/dce.2025.10012
- React-NIF: A neural implicit flow-based framework for complex fuel combustion chemistry acceleration
- Amarathunga, D. V., & Echekki, T. (2025, December 29), Fuel. https://doi.org/10.1016/j.fuel.2025.138166
- A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels
- Alqahtani, S., Gitushi, K. M., & Echekki, T. (2024, February 4), Energies, Vol. 17. https://doi.org/10.3390/en17030734
- A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements
- Taassob, A., Kumar, A., Gitushi, K. M., Ranade, R., & Echekki, T. (2024, June 28), Computer Methods in Applied Mechanics and Engineering, Vol. 429. https://doi.org/10.1016/j.cma.2024.117163
- Combustion chemistry acceleration with DeepONets
- Kumar, A., & Echekki, T. (2024, February 15), Fuel, Vol. 365. https://doi.org/10.1016/j.fuel.2024.131212
