Samuel Matthiesen

(née G. Fadel)
Postdoc Researcher, Linköping University
Campus Valla, B-huset, Entrance 27


My main research interests are on unsupervised machine learning, particularly with geometrically-inspired representations. I investigate how the geometry of representations learned by different models can be either enforced or understood through the formalism of Riemannian manifolds.

During my PhD, I worked on one of the first temporal graph neural networks for recommendation settings. In generative modeling, I investigated how to improve the generation of data via interpolations in the base space of normalising flows.

Prior to my PhD research, my research interests focused on data visualization, with an emphasis on making high-dimensional data understandable through interactive 2D scatterplots.


I have been regularly teaching courses every semester since 2020. I mostly teach for graduate (MSc. level) courses, but in the past I have worked as a teaching assistant for BSc. courses.

When opportunity allows it, I also try to come up with alternative ways to help students understand some concepts. Particularly for a Deep Learning course, I have designed a visualization tool for convolutions. I would be happy to hear if you found it useful or have feedback.



Postdoc researcher (2023─present)
Postdoc researcher (2021─2023)
Visiting researcher (2019─2020, 15 months)
Ph.D. in Computer Science (2016─2021)
Visiting researcher (2015─2016, 3 months)
M.Sc. in Computer Science (2014─2016)
B.Sc. in Computer Science (2010─2013)