Furkan Yakal

I recently completed my M.Sc. in Informatics with a specialization in Engineering Software-Intensive Systems at Technical University of Munich, graduating with an overall grade of 2.0. My academic foundation was established at Koç University where I completed dual B.Sc. degrees in Industrial Engineering and Computer Science with a track program in Artificial Intelligence, graduating Magna Cum Laude with honors.

Alongside my studies, I gained valuable professional experience at leading organizations including BMW Group and EY, where I contributed to diverse projects spanning data engineering, applied AI, software engineering, and process optimization. My work encompasses designing scalable data pipelines, engineering cloud environments, developing web applications, and implementing full-stack solutions that bridge theoretical concepts with practical business challenges.

In my master’s thesis, “A Resilient Matrix Assembly Layout Design for Mixed-Model Vehicle Production,” defended in September 2025 with a grade of 1.3, I formulated one of the first stochastic optimization models to address task-level disruptions in automotive manufacturing. This work contributed to the literature by introducing the Resilient Matrix Assembly Layout Problem, demonstrating significant reconfiguration cost reductions compared to traditional static models and highlighting the value of proactive, disruption-aware planning.

My interest lies at the intersection of AI, data, and software engineering, where rigorous modeling meets the realities of production systems. I aim to build solutions that are technically sound, resilient, and genuinely useful.