Christian Naesseth is a tenured Assistant Professor at the Amsterdam Machine Learning Lab with 11 years of experience bridging probabilistic modeling, approximate inference, and applied control. His academic path—PhD in Electrical Engineering and roles at Columbia, Linköping, and visits to Oxford and Blei's lab—reflects deep expertise in Monte Carlo methods and generative models applied to real-world sensing problems. He has a strong teaching and supervision background, having led courses and supervised masters projects in automatic control and modelling. Early industry work on sensor fusion for Autoliv and a Microsoft research internship demonstrate his ability to translate theory into robust, safety-critical systems. Known for combining rigorous theory with practical experiments, he often focuses on improving reliability and uncertainty quantification in perception and decision systems. Based in Amsterdam, he brings an international perspective shaped by study and research experiences across Europe, China, and the US.
11 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BSc), Mathematics, Bachelor of Science (BSc), Mathematics at Linköping University
Exchange program, Electrical Engineering, Exchange program, Electrical Engineering at Beijing Institute of Technology
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