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Fredrik Sandin
Fredrik Sandin

Fredrik Sandin

Luleå University of Technology
Machine Learning
Embedded Intelligent Systems LAB
Department of Computer Science, Electrical and Space Engineering
+46 (0)920 493163
A3573 Luleå


We work in both application-driven research projects where machine learning, artificial intelligence and computational physics are used to solve challenging problems, as well as in related fundamental research projects.

I'm interested in brain-inspired computing principles and neuromorphic technologies that offer fundamentally new ways to process information and implement learning for artificial intelligence (AI) purposes, for example in application domains requiring high energy efficiency such as the Internet of Things (IoT). The present computing systems and deep learning approaches are resource intensive and do not offer a sustainable path for large-scale AI in the digitization of society. 

More info: Google scholar profileSemantic scholar profile.


PhD students

Graduated PhD students


  • Daniel Strömbergsson, Co-design optimization of neuromorphic condition monitoring sensor system.
  • Saad Arif, starting in January.

Past Postdocs

  • Sergio Martin del Campo Barraza (now adjunct senior lecturer in our group), unsupervised machine learning for automation of wind turbine condition monitoring system, two years funded by SKF.


Examiner of

  • Programming for Machine Learning (D0036E).
  • Neural Networks and Learning Machines (D7046E).
  • Neuromorphic Computing, upcoming course in the Applied AI program and Applied AI master.
  • Programming for Scientific Computing, upcoming course in the Engineerng Physics and Electrical Engineering program.
  • Master thesis courses in Engineerng Physics and Electrical Engineering (X7010E, X7011E, X0003E).

Since 2014, I am program director of the "Teknisk fysik och elektroteknik" program at LTU (Civilingenjör, 300Hp), co-directed with Andreas Almqvist.

Lectures also in D0032 Introduction to AI and D0028E Programming and Digitalization. Was examiner for E0003E Electric circuit theory, D0011E Digital design, D0017E Introduction to programming for engineers. Before 2010 I taught courses in physics, including lectures about mechanics, thermodynamics, waves, optics, quantum physics, and also some 3rd-cycle lectures in astrophysics, cosmology and quantum field theory at finite temperature (J. Kapusta's book).


MSc diploma work in ATLAS at CERN (2001). PhD in Physics (2007, LTU, Swedish Graduate School of Space Technology). I received the 2004 “New-Talents” award for original work in theoretical physics at the International School of Subnuclear Physics in Erice. First postdoc at IFPA in Belgium (2008-2009), focusing on computational physics. A growing interest for the physics of brains and neuromorphic technologies made me shift research focus. I did a second postdoc in brain-like machine learning (2010-2011) at EISLAB, where I became assistant professor (until April 2016), associate professor (until March 2021) and presently work as professor in Machine Learning. I was awarded the Gunnar Öquist Fellowship from the Kempe Foundations in 2014 which included 3 MSEK for research. I served as a technical committee member of the SKF–LTU University Technology Center, as a research theme leader of the Intelligent Industrial Processes area of excellence, and as a member of the Applied AI Innovation Hub. I served in several faculty workgroups focusing on improving education processes. I am a member of ELLIS since June 2022.


Some code and tools. Contact me if you are interested in code used in research articles that are 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 the highly cited paper in Phys. Rev. D.
  • Femtolensing tool, link . Calculates the gravitational lensing signatures of low-mass (10 14 -10 17 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, related publications here and here.
  • 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 Sharelatex / Overleaf etc.

Some highlights


Conference paper

Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics (2023)

Sjögren. O, Grund Pihlgren. G, Sandin. F, Liwicki. M
Part of: Proceedings of the Northern Lights Deep Learning Workshop 2023, Septentrio Academic Publishing, 2023
Conference paper

Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms (2022)

Javed. S, Javed. S, van Deventer. J, Sandin. F, Delsing. J, Liwicki. M, et al.
Part of: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), Institute of Electrical and Electronics Engineers (IEEE), 2022
Article, review/survey

Deep-learning-based vision for earth-moving automation (2022)

Borngrund. C, Sandin. F, Bodin. U
Part of: Automation in Construction
Article in journal

Failure mode classification for condition-based maintenance in a bearing ring grinding machine (2022)

Ahmer. M, Sandin. F, Marklund. P, Gustafsson. M, Berglund. K
The International Journal of Advanced Manufacturing Technology, Vol. 122, s. 1479-1495