MSCA PF: 3D Deep Generative Models for Personalized Medical Device Optimization

I hold a Bachelor’s and Master’s degree in Architecture from Middle East Technical University (METU), and I am currently in the final stage of my PhD in Informatics at Istanbul Technical University (ITU). I also serve as a Faculty Lecturer in the Digital Game Design Department at Istanbul Bilgi University, where I teach game development and XR system development. My research lies at the intersection of computational design, artificial intelligence, and real-time 3D systems. In parallel, I co-founded Orbycra, a TÜBİTAK-funded research-driven medical technology venture established at ITU ARI Technopark, in collaboration with a neurosurgeon and a developer/designer team. Through our GraftEngine project, we develop AI-driven 3D reconstruction pipelines from medical imaging data to enable personalized cranial graft and graft mold design. Our work integrates deep generative 3D modeling, parametric design automation, and hardware–software supported digital twin systems for patient-specific medical device optimization.We have also developed immersive AR and VR applications for surgical planning and volumetric data interaction, allowing clinicians to explore, simulate, and refine device geometries before production. Our research and R&D efforts focus on automating patient-specific geometry generation, improving precision in device manufacturing, and reducing manual design dependency through AI-based modeling approaches.

The proposed MSCA Postdoctoral Fellowship aims to advance deep generative 3D models for personalized medical device optimization by integrating AI-driven geometry synthesis, parametric control frameworks, and immersive XR-based interactive validation environments. The project will explore intelligent design automation pipelines and adaptive digital twin systems to support translational medical research, surgical planning, and rapid prototyping workflows. By combining computational design, machine learning, immersive technologies, and clinical collaboration, this research seeks to establish next-generation AI-enabled personalized medical device development systems that bridge advanced digital modeling with real-world clinical impact.