As a Computer Vision Engineer at AGTEKS LTD., I've been instrumental in optimizing fabric production processes, with a primary focus on developing and refining algorithms for defect detection at various production stages. Utilizing techniques such as texture analysis, feature extraction, and anomaly detection, I've contributed significantly to enhancing the efficiency and quality standards of our operations.Key Responsibilities:- Conducting thorough literature reviews to remain updated on cutting-edge computer vision and defect detection methodologies.- Innovating and combining existing algorithms to address specific challenges in fabric defect detection.- Continuously refining and optimizing algorithms to ensure adaptability to different fabric types and manufacturing conditions.In addition to my core responsibilities, I've spearheaded initiatives to leverage GPU computing for accelerated processing. By migrating select processes to GPU architectures, particularly on Jetson iGPUs, I've achieved remarkable speed enhancements, enabling real-time defect detection and analysis. This optimization effort has not only significantly improved system responsiveness but has also laid the foundation for future scalability and performance advancements, marking a pivotal advancement in our technological capabilities.Furthermore, I've extensively utilized C++, TensorRT, OpenCV, and other tools to implement and deploy these algorithms efficiently. Leveraging the power of these technologies, I've streamlined our development pipeline and optimized performance across various hardware platforms, contributing to our mission of delivering robust and scalable computer vision solutions for fabric production.methods.

During my tenure as a Research Engineer at Boğaziçi University's Intelligent Systems Laboratory, I played a pivotal role in a research project focused on Semantic Scene Analysis Through Visual Exploration And Learning. This project, which formed the core of my master's studies, provided me with extensive hands-on experience in intelligent systems and computer vision.Key Contributions:- Led the development and implementation of Semantic Scene Analysis Through Visual Exploration And Learning, a project aimed at advancing the understanding of scene perception and interpretation through visual data within the ROS framework.- Spearheaded the design and optimization of various components of the project, including object detection methodologies, scene exploration techniques, visual encoding algorithms, and hierarchical clustering processes, all integrated seamlessly into the ROS ecosystem.- Collaborated closely with a diverse team of researchers and engineers, leveraging collective expertise to tackle complex challenges and drive innovation in the field of computer vision and robotics, with ROS as the central platform for integration and deployment.- Demonstrated proficiency in utilizing ROS alongside Matlab, C++, and Python to develop and integrate robust solutions, ensuring the project's scalability, adaptability, and compatibility with ROS-enabled robotic systems.My experience at the Intelligent Systems Laboratory not only deepened my technical skills but also fostered a collaborative and innovative mindset essential for successful research endeavors within the ROS ecosystem. I am enthusiastic about leveraging this expertise in future endeavors, contributing to advancements in intelligent systems and robotics powered by ROS.

During my internship at Aselsan, a leading defense electronics company in Turkey, I had the opportunity to work on implementing the reliable data transfer 3.0 protocol to CC1120 and CC1125 transceivers using C programming language. Aselsan is renowned for its cutting-edge research and development in defense technologies. This experience provided me with valuable hands-on experience in embedded systems and communication protocols within a high-tech and dynamic environment. I gained insights into the complexities of ensuring reliable data transfer in real-world applications. My internship at Aselsan not only enhanced my technical proficiency but also provided me with exposure to working in a professional environment within the field of telecommunications and defense electronics.

During my internship at TUBITAK İLTAREN (The Scientific and Technological Research Council of Turkey, Informatics and Information Security Research Center), I had the opportunity to apply theoretical knowledge to practical problems by implementing the Random Forest Algorithm using Matlab. This involved coding, testing, and debugging the algorithm, as well as generating synthetic datasets to evaluate its performance comprehensively. Through this experience, I gained valuable insights into machine learning techniques and enhanced my proficiency in programming and data analysis.