Summary
Johan Wessén is a lead developer and optimization specialist with eight years of experience applying continuous and combinatorial optimization, Bayesian methods and machine learning to real-world systems such as virtual power plants and industrial robots. Currently driving VPP optimization at CheckWatt and advising on GreenTech data at OptimaPlanta, he blends research-grade methods (KTH PhD work) with hands-on production deployments in cloud and embedded settings. His strengths include statistically sound adaptive sampling and Bayesian optimization with Gaussian processes, plus scalable learning via deep and reinforcement learning for scheduling, routing and coordination of embodied agents. Johan is as comfortable tuning live energy systems as he is developing discrete event control for autonomous robots, and he thrives in collaborative, prestige-free environments that favor practical innovation. A member of the invite-only Nova network, he combines a strong academic foundation with a pragmatic focus on decarbonization and automation.
8 years of coding experience
13 years of employment as a software developer
Exchange studies, Engineering, Exchange studies, Engineering at National University of Singapore
KTH Royal Institute of Technology
Master of Science (MSc), Electrical and Electronics Engineering, Master of Science (MSc), Electrical and Electronics Engineering at Uppsala universitet
Masters of Science in Engineering Physics, Bachelor of Social Science (Major: Business Economics), Engineering Physics, Business Economics, Masters of Science in Engineering Physics, Bachelor of Social Science (Major: Business Economics), Engineering Physics, Business Economics at Uppsala University
Swedish, English