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In the Quality Assurance department, I develop machine learning-driven systems for flatness evaluation, defect and anomaly detection in steel production. My work focuses on designing segmentation and anomaly detection models to automate quality control and enhance accuracy.
Developed curricula and instructional materials for courses in quantitative methods, financial modeling, operations research simulation, database design, and programming. Conducted research in cybersecurity, the Internet of Things, and machine learning.
Designed and implemented statistical and machine learning models to detect anomalies in network information system performance.
Conducted research on networking infrastructure technologies, analyzing and evaluating their suitability for specific purposes.
