Michel Gokan Khan

Postdoc in ML-Driven Optimization of Production in Industry 5.0

Michel is currently a Postdoctoral Researcher at the KTH Royal Institute of Technology under the Digital Futures Industrial and Societal Partnership Programme (ISPP) project "SMART – Smart Predictive Maintenance for the Pharmaceutical Industry" funded by Digital Futures and AstraZeneca, concentrating on applying machine learning to optimize production processes towards Industry 5.0. His main research interest lies in ML-based optimization of large-scale cloud and edge systems. His research currently focuses on machine learning, computer vision, data-driven methods in production line optimization, and AI-assisted digital twins.

He holds a PhD in Computer Science from KAU Sweden, with his research focused on leveraging machine learning for optimizing large-scale cloud native systems.

Michel has a background in both the academic and industrial sectors of technology. His experience includes serving as the Chief Technology Officer, team lead, and co-founder in various companies and engaging in research at companies such as Ericsson, during which he developed and patented a method in performance modeling and optimization for cloud native systems and also multiple contributions later utilized in 5G core prototypes. He has also participated in various types of robotic competitions including Robocup soccer simulation leagues.

His scholarly work includes extensive research on cloud, edge, and fog system performance optimization, and edge computing, contributing to some patents, journal articles, conference papers, and book chapters in the field. These publications cover a wide range of topics, including cloud computing, machine learning, and microservice architectures.

Michel has received awards for his research, including the IEEE Best Paper Award at NetSoft '20, IEEE Best Demo in NFV-SDN '18, the Young Professional of the Month Award by IEEE Sweden in 2019, and more. His publication record focuses on improving and understanding the complexities of cloud computing and machine learning technologies, specifically in the realm of simulation, modeling, digital twinning, and optimization techniques.

In addition to his research, Michel is committed to education and mentorship, having lectured on distributed systems, databases, and computer networking, as well as supervising thesis projects. His technical expertise, spanning cloud computing platforms, machine learning, optimization, data-driven methods, digital twinning, programming, and frameworks, forms the foundation of his research and teaching activities.

Github: https://github.com/michelgokan/
LinkedIn page: https://www.linkedin.com/in/michelgokan/
Twitter: https://x.com/michelgokan
Google Scholar: https://scholar.google.com/citations?user=LJV0pHQAAAAJ

Personal Homepage: https://michelgokan.github.io/