Jakob is a part of the Public Interest AI research group at Alexander von Humboldt Institute for Internet and Society (HIIG), which he joined in February 2021. His research is primarily focused on machine learning and artificial neural networks, particularly for computer vision.
Jakob graduated from the University of Copenhagen (UCPH) in 2020. He did both his bachelor and master in mathematics. The bachelor was primarily focused on theoretical mathematics, and he did his thesis on applications of groupoid theory in algebraic topology. During his Master’s, the focus pivoted to more applied mathematics in combination with a number of computer science courses, primarily in the areas of cryptography and machine learning. His thesis was on the latent space geometry of deep generative neural networks, investigating a manifold atlas interpretation of hybrid discrete-continuous latent spaces.
Prior to starting at HIIG, Jakob worked as a research assistant at UCPH, continuing the research of his Master thesis.
In his dissertation at HIIG, Jakob investigates the possibility of using machine learning and computer vision tools to map barrier-free accessibility of public spaces.
Researcher: AI & Society Lab