Mechanical Engineer & Software Developer
Motivated Mechanical Engineer eager to contribute to innovative industrial automation solutions. Passionate about learning advanced manufacturing technologies and collaborating with diverse teams to solve complex client applications in a dynamic, international environment.
I’m a Mechanical Engineer with a passion for industrial automation and advanced manufacturing technologies, and I’m also a software developer who loves solving problems. My experience ranges from developing digital twin monitoring software to optimizing manufacturing processes in engineering roles. I create innovative software solutions for various projects. I thrive in international, collaborative environments and am always eager to tackle complex technical challenges.
M.Sc. Thesis (2025). Proposed a novel structural health monitoring (SHM) approach for detecting and localizing damage in composite materials. The developed system integrates spatial coordinate-based localization with ply-level damage identification. Sensor data are processed through an ensemble of machine learning models designed to detect and localize damage with high precision. A two-stage framework is introduced to improve robustness, allowing different models to specialize in damage detection at various ply levels.
B.Sc. Thesis (2022). Pilot-induced oscillations (PIO) are rare but dangerous phenomena that have existed since the early stages of human air flights. This study offers a new approach by employing machine learning to detect pilot-induced oscillations by analyzing signals from the aircraft. Different PIO occurrences are modeled and datasets are trained to detect PIOs. Results of machine learning with different algorithms and their comparison are provided.
DIMOSS is an integrated structural monitoring software based on discrete strain measurements for aerospace, civil, and marine structures. It includes all the necessary tools to design and implement an effective system for monitoring the displacements, strains, and stresses experienced by a structure during its operational life. As a research fellow, I collaborated by editing the MATLAB and Python scripts and handled the transfer process from MATLAB to Python.
M.Sc. Thesis (2022). Proposed a novel structural health monitoring (SHM) approach for detecting and localizing damage in composite materials. The developed system integrates spatial coordinate-based localization with ply-level damage identification. Sensor data are processed through an ensemble of machine learning models designed to detect and localize damage with high precision. A two-stage framework is introduced to improve robustness, allowing different models to specialize in damage detection at various ply levels.
B.Sc. Thesis (2022). Pilot-induced oscillations (PIO) are rare but dangerous phenomena that have existed since the early stages of human air flights. This study offers a new approach by employing machine learning to detect pilot-induced oscillations by analyzing signals from the aircraft. Different PIO occurrences are modeled and datasets are trained to detect PIOs. Results of machine learning with different algorithms and their comparison are provided.
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