Fredrik Sandin
Fredrik Sandin

Fredrik Sandin

Biträdande professor
Luleå tekniska universitet
Institutionen för system- och rymdteknik
A2304 Luleå


I work in the domain of machine learning and neuromorphic engineering at EISLAB since 2010, with a special interest for embedded intelligence and information processing principles motivated by results in neuroscience. I currently work on projects related to neuromorphic systems and machine learning for:

  • Condition monitoring.
  • Sub-milliwatt spatiotemporal pattern recognition for IoT.
  • Automation of heavy construction equipment.
  • System interoperability in IIoT and Industry 4.0.
  • Automation of edge datacenters.
  • High-quality steel manufacturing.

I studied in the Swedish Graduate School of Space Technology and have a PhD in Physics (2002-07) and I did a postdoc at IFPA in Belgium (2008-09), both focusing on numerical simulations and modelling in theoretical physics (finite temperature and density field theory, mean field models, general relativity in compact objects). I did the MSc diploma work in ATLAS at CERN (2001). My curiosity for brains and neuromorphic engineering 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 the AI Innovation Hub and the SKF-LTU University Technology Center.

Link to scholar profile.


I am the examiner of the following courses:

  • Electric circuit theory (E0003E), link.
  • Neural networks and learning machines (D7046E), link.
  • Introduction to programming for engineers (D0017E), link.

Since 2014, I coordinate the "Teknisk fysik och elektroteknik" program at LTU (Civilingenjör, 300Hp). Before 2010 I taught several courses in physics.


Current PhD students

  • Jacob Nilsson, machine learning for M2M interoperability in the Arrowhead Framework.
  • Mattias Nilsson, architecture for low-power pattern recognition with DYNAP neuromorphic system.
  • Kim Albertsson (at CERN), machine learning in high-energy physics.
  • Rickard Brännvall (at RISE), machine learning for automation of datacenters, AutoDC.
  • Co-supervision of Gustav Grund Pihlgren, Carl Borngrund and Muhammad Ahmer.

Current Postdocs

  • Sergio Martin del Campo Barraza, machine learning for automation of wind turbine condition monitoring system.

Graduated PhD students

  • Siddharth Dadhich, Automation of Wheel-Loaders, link.
  • 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 large 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 Sharelatex, Overleaf etc.

Some highlights.


Artikel i tidskrift

Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning (2019)

Martin del Campo Barraza. S, Schnabel. S, Sandin. F, Marklund. P
Tribology International, Vol. 132, s. 30-38
Artikel i tidskrift

Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders (2019)

Dadhich. S, Sandin. F, Bodin. U, Andersson. U, Martinsson. T
Automation in Construction, Vol. 97, s. 1-12

Interoperability automation considered as machine learning tasks (2019)

Nilsson. J, Sandin. F, Delsing. J
Paper presented at : 2nd Productive4.0 Consortium Conference, Budapest, March 12-14, 2019

Kinematic Frequencies of Rotating Equipment Identified with Sparse Coding and Dictionary Learning (2019)

Martin del Campo Barraza. S, Sandin. F, Schnabel. S
Ingår i: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019, 2019

Dataset concerning the vibration signals from wind turbines in northern Sweden (2018)

Martin del Campo Barraza. S, Sandin. F, Strömbergsson. D