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My research focuses on Circular Manufacturing Decision Intelligence: developing decision support that makes circular manufacturing operationally feasible, scalable, and economically viable. I build on expertise in the design, planning, and control of manufacturing systems, and develop data-driven optimization approaches (including learning-augmented metaheuristics) to support complex decisions under uncertainty and quality constraints. My work targets manufacturing-specific circularity challenges such as heterogeneous quality of returned parts, variable recovery routes, and tight requirements on performance, traceability, and like-new outcomes, as well as the need for in-situ monitoring and control to detect deviations early and adapt decisions during production. The aim is to translate these methods into implementable decision-support tools for industry.

I lead WP1 of the national project -- a consortium of 15 PhD projects across 7 universities with 20+ industrial partners -- focused on Advancing Dutch Circular Economy through Additive Manufacturing: Strategies for Repair and Remanufacturing using AM, as a pathway to scalable circular manufacturing.

Circular manufacturing succeeds only when circularity becomes operational! Through decision intelligence that makes recovery, quality, and performance work at scale.

I am an Assistant Professor in Circular Manufacturing at Eindhoven University of Technology (ºÚÁϸ£ÀûÍø). I obtained my BSc (2009, cum laude, University of Tabriz, Iran) and MSc (2011, cum laude, University of Tehran, Iran) in Industrial Engineering, followed by two PhD degrees completed in 2015: a PhD in Industrial Engineering (University of Tehran, Iran) and a PhD in Mechanical Engineering (École Nationale Supérieure d’Arts et Métiers, France). From 2015 to 2017, I conducted postdoctoral research at École des Mines de Saint-Étienne (France) on the performance evaluation of semiconductor manufacturing. Before joining ºÚÁϸ£ÀûÍø, I was an Associate Professor (2017–2023) at IMT Atlantique (France) in Operations Research and Machine Learning.