Fatih Arda Zengin is an M.Sc. candidate in Manufacturing Engineering at Sabancı University with a background in Industrial Engineering. His research interests lie at the intersection of artificial intelligence, energy technologies, and advanced manufacturing.
His current research focuses on machine learning and deep learning methods for battery health monitoring and remaining useful life (RUL) prediction, with particular interest in developing accurate and computationally efficient models suitable for real-time engineering applications. His work explores topics including recurrent neural networks, model optimization, quantization, and data-driven approaches for battery intelligence.
His research experience also includes physics-guided artificial intelligence and machine learning for semiconductor device modeling, including the design and optimization of GaN transistor technologies. His broader interests include battery management systems, edge and embedded AI, semiconductor technologies, energy systems, and AI-enabled manufacturing.
Through his research, he aims to develop practical machine learning methods that connect data-driven modeling with real-world engineering and energy applications.