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Fredrik Sandin
Fredrik Sandin

Fredrik Sandin

Luleå tekniska universitet
Institutionen för system- och rymdteknik
A2304 Luleå


Machine learning and neuromorphic computing. See my scholar profile for references.

I have a PhD in Physics (2007, Swedish Graduate School of Space Technology) and I did a postdoc at IFPA in Belgium (2008-09), both focusing on numerical simulations and modelling in theoretical physics. I did the MSc diploma work in ATLAS at CERN (2001). My curiosity for brains, neuromorphic engineering and the physics of consciousness made me shift research focus. I did a second postdoc in brain-inspired machine learning (2010-11) at EISLAB, where I presently work and are involved in e.g. the AI Innovation Hub.

NCE focus issue: applications of neuromorphic engineering to wireless networks for distributed sensing.


I am the examiner of the following courses:

  • Neural networks and learning machines (D7046E), link.
  • Neuromorphic computing, upcoming course in the Applied AI program.

Since 2014, I coordinate the "Teknisk fysik och elektroteknik" program at LTU (Civilingenjör, 300Hp). In the past I was the examiner for E0003E Electric circuit theory, D0011E Digital design, D0017E Introduction to programming for engineers. Before 2010 I taught several courses in physics.


Current PhD students

Past Postdocs

Graduated PhD students

  • Siddharth Dadhich, Automation of Wheel-Loaders, link (co-supervisor).
  • Sergio Martin del Campo Barraza, Unsupervised feature learning applied to condition monitoring, link.
  • Blerim Emruli, Ubiquitous Cognitive Computing: A Vector Symbolic Approach, link.


Some code and tools. Contact me if you are interested in code used in research articles that is not listed below.

  • 3FCS code, link. This code was developed and used for the quark matter calculations in the papers about neutron stars with quark matter cores and related phase diagrams, including my most cited paper in Phys. Rev. D.
  • Femtolensing tool, link. Calculates the gravitational lensing signatures of low-mass (1014-1017 kg) compact interstellar objects. This code was developed when working on the preon star hypothesis, see highlights below.
  • N-dimensional random projection code, link. Result of early work on representation learning for cognitive computing.
  • CBVS code, link. Early work on cognitive computing.
  • Dircheck, link. A useful tool for verification of file archives.
  • Python code for distributed computing over ssh, link.
  • ASOUND Matlab plugin, link.
  • Online collaborative writing in Latex, link. Developed before the time of Overleaf etc.

Some highlights


Artikel i tidskrift

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning (2021)

Martin-del-Campo. S, Sandin. F, Schnabel. S
Journal of Risk and Reliability
Artikel i tidskrift

Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings (2021)

Martin-del-Campo. S, Sandin. F, Strömbergsson. D
International Journal of Computational Intelligence Systems, Vol. 14, nr. 1, s. 106-121

Pretraining Image Encoders without Reconstruction via Feature Prediction Loss (2021)

Grund Pihlgren. G, Sandin. F, Liwicki. M
Ingår i: Proceedings of ICPR 2020, 25th International Conference on Pattern Recognition, s. 4105-4111, IEEE, 2021

Adaptation of a wheel loader automatic bucket filling neural network using reinforcement learning (2020)

Dadhich. S, Sandin. F, Bodin. U, Andersson. U, Martinsson. T
Ingår i: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, 20563

Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering (2020)

Nilsson. J, Delsing. J, Sandin. F
Ingår i: IEEE 24th International Conference on Intelligent Engineering Systems, Proceedings, s. 139-144, IEEE, 2020