Hoppa till innehållet
Fredrik Sandin
Fredrik Sandin

Fredrik Sandin

Biträdande professor
Luleå tekniska universitet
Cyberfysiska system
EISLAB
Institutionen för system- och rymdteknik
Fredrik.Sandin@ltu.se
0920-493163
A2304 Luleå

Research

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.

Teaching

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.

Supervision

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.

Other

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.

Publikationer

Konferensbidrag

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), Conference Proceedings, IEEE, 2020, 20563
Konferensbidrag

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
Konferensbidrag

Fault Severity Estimation using Weak Supervision with Language Based Labels and Condition Monitoring Data (2020)

Ekström. K, Sandin. F
Paper presented at : The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS), 16-17 June, 2020, Online
Konferensbidrag

Improving Image Autoencoder Embeddings with Perceptual Loss (2020)

Grund Pihlgren. G, Sandin. F, Liwicki. M
Ingår i: 2020 International Joint Conference on Neural Networks (IJCNN), Conference Proceedings, IEEE, 2020, 20229
Konferensbidrag

Machine Vision for Construction Equipment by Transfer Learning with Scale Models (2020)

Borngrund. C, Bodin. U, Sandin. F
Paper presented at : 2020 International Joint Conference on Neural Networks (IJCNN), 19-24 July, 2020, Glasgow, United Kingdom