Hasan Tercan

Dr.-Ing. Hasan Tercan is a Scientific Researcher and Head of the Research Field 'Industrial Deep Learning' at the Institute for Technologies and Management of Digital Transformation, University of Wuppertal, where he has been since December 2018. His work focuses on the development and application of machine learning and artificial intelligence methods in industrial environments, particularly in production quality assurance and intelligent process control.

Education:

• PhD (Dr.-Ing.) in Computer Science, University of Wuppertal (2023)
• Master's in Computer Science, Technical University of Darmstadt (specialization: Database Systems and Data Mining)
• Research Assistant, Chair of Information Management in Mechanical Engineering, RWTH Aachen University

His research interests lie at the intersection of machine learning and industrial applications. He specializes in transfer learning, lifelong learning, deep reinforcement learning, and predictive quality modeling in manufacturing. A key focus is bridging simulation and real-world data through simulation-to-reality approaches and enabling continuous model adaptation in dynamic production environments.

His recent publications (2025) reflect a strong trend in applying advanced AI techniques—such as AttentiveGRUs, GANs, Decision Transformers, and deep reinforcement learning—to industrial challenges like radar-based object detection, job shop scheduling, robot end-effector control, and process configuration. These works demonstrate a consistent emphasis on deploying scalable, adaptive AI solutions in real-world industrial systems.

Scientific Awards:

• Ph.D. Prize from the Friends and Alumni Association of the University of Wuppertal (FABU)

Hasan Tercan leads the 'Industrial Deep Learning' research group, indicating active mentoring and project leadership. While specific grant details are not mentioned, his multiple 2025 publications in high-impact journals (e.g., Procedia CIRP, AI, Autonomous Agents and Multi-Agent Systems) suggest involvement in funded research projects. He collaborates closely with Prof. Dr. Meisen and other researchers, contributing to a robust research ecosystem in industrial AI.

He is affiliated with the 'Industrial Deep Learning' research group, which focuses on advancing deep learning methodologies tailored for industrial transformation, including intelligent planning, control, and quality assurance in manufacturing and assembly processes.