Daniel Felipe Perez Ramirez

Daniel Felipe Perez Ramirez is an industry doctoral student at KTH Royal Institute of Technology, affiliated with the Division of Software and Computer Systems within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning to solve combinatorial optimization problems in networked systems, emphasizing scalability and generalization. He is involved in the SSF project 'Instant Cloud Elasticity' and the Horizon 2020 AI@Edge project.

• Education:
• B.Sc. in Mechanical Engineering (Technical University of Munich)
• M.Sc. in Robotics, Cognition, Intelligence (Technical University of Munich)

His research interests span machine learning applications in combinatorial optimization, resource management for networked systems, and scalability challenges in AI. Prior to joining KTH, he worked at RISE Research Institutes of Sweden AB as a research engineer, focusing on applied machine learning for automotive industries, robotics sensor integration, and agile product development methods.

Advisors: Prof. Dejan Kostic and Prof. Magnus Boman.
Labs/Teams: Collaborates with RISE and KTH’s research divisions in networked systems.