
Fredrik Sandin
Professor
Research subject: Machine Learning
Division: Embedded Intelligent Systems LAB
Department of Computer Science, Electrical and Space Engineering
-
Luleå, A3573
About
I'm a physicist interested in the principles of cognition and how brain-like neuromorphic technologies External link. can be used to improve the efficiency and performance of sensors (machine perception) and artificial intelligence (AI) from edge to cloud: Google scholar
External link., Linked In
External link., LTU Machine Learning
External link..
Contact
If you have an urgent matter please phone me from a number where I can reach you. To initiate contact please book a meeting here External link. or via the LTU staff calendar. I am unable to attend to all email messages.
Research
We work in both challenge-driven applied and basic research projects where machine learning, artificial intelligence and computational physics are used to solve challenging problems, often in cooperation with research institutes and industry.
For example, we are working on co-design optimization of sparse sampling and neuromorphic–digital sensor systems, such as sensors for intelligent fault diagnosis (IFD) and future high-energy physics (HEP) detectors. Another focus area is seamless integration of neuromorphic systems in the digital infrastructure, including addressing challenging problems such as dynamic service data interoperability and distributed processing of information where it is most efficient. We are also working on conventional deep learning projects, such as industrial chatbots External link. for agent-based decision support, and deep learning in edge devices (sparse sampling, online learning, few parameters, etc). Active projects:
- Fast, Robust, and Efficient NanoPhotonic Neuromorphic Computing (WASP-WISE, LiU-2023-00139
External link.), with NanoLund
External link..
- Hybrid Digital-Neuromorphic Computing Services: Interoperability, Interfacing and Programming (Vinnova, 2023-01363
External link.), with RISE
External link..
- Differential Optimization of Hybrid Neuromorphic-Digital Systems for Energy-Efficient Machine Learning (Jubileumsfonden och Kempestiftelserna, JCSMKJF23-0003
External link.), with TVM
External link. and INFN
External link. in Padova.
- Two postdoc projects, one funded by Creaternity
External link. and one by Kempestiftelserna (JCSMK23-0218).
The present deep learning approach is resource intensive External link. and hence exclusive. Digital analytics works well in high-fidelity problem spaces, but is too power intensive for perception and cognition in noisy environments. The integration of complementary approaches like neuromorphic computing, nanophotonic computing and sub-Nyquist analog analytics is required for the long-term welfare of society in the era of AI
External link.. For general motivations of this viewpoint see for example this
External link. and this
External link. report.
Completed research projects/contributions include:
- Vinnova VALD (FFI, dnr 2021-05035, 3.4 MSEK), KnowIT Fast
External link. (PiiA, dnr 2019-02533, 4.6 MSEK). ALDEE (FFI, dnr 2019-03073, 3.5 MSEK), MetMaskin (dnr 2018-02666, 4.7 MSEK).
- EU ITEA3 AutoDC (dnr 2018-02232, 58 MSEK), ECSEL JU Arrowhead Tools (no 826452, 84 MEUR).
- The Kempe Foundations NCS (dnr SMK-1429 and JCK-1809, 4.4 MSEK).
- STINT Institutional Grant (dnr IG2011-2025, 1.5 MSEK).
Active PhD students:
- Karl Löwenmark
External link., technical language supervision for intelligent fault diagnosis, GPTech
External link..
- Rickard Brännvall
External link. (RISE
External link.), homomorphic encryption machine learning, worked in AutoDC
External link..
- Co-supervision of Saleha Javed
External link., Awais Khan
External link. (joint PhD with Tommaso Dorigo
External link.), and Lars-Johan Sandström
External link..
Graduated PhD students:
- Carl Borngrund (2024), Towards Deep-learning-based Autonomous Navigation in the Short-loading Cycle (thesis
External link., co-supervisor).
- Gustav Grund Pihlgren (2023), Deep Perceptual Loss and Similarity (thesis
External link., co-supervisor).
- Mattias Nilsson (2023), Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems (PI, thesis
External link.).
- Muhammad Ahmer (2023), Intelligent fault diagnosis and predictive maintenance for a bearing ring grinder, (thesis
External link., co-supervisor, industrial PhD at SKF).
- Jacob Nilsson (2022), Machine Learning Concepts for Service Data Interoperability (PI, thesis
External link.).
- Kim Albertsson (2021), Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA (thesis
External link., CERN funded, in collaboration with Andreas Hoecker
External link.).
- Siddharth Dadhich (2018), Automation of Wheel-Loaders (thesis
External link., co-supervisor).
- Sergio Martin del Campo Barraza (2017), Unsupervised feature learning applied to condition monitoring (PI, thesis
External link.).
- Blerim Emruli (2014), Ubiquitous Cognitive Computing: A Vector Symbolic Approach (PI, thesis
External link.).
Postdocs:
- Daniel Strömbergsson
External link., Co-design optimization of neuromorphic condition monitoring sensor system.
- Neuromorphic computing postdoc grant (Kempe Foundations), recruiting.
Former Postdocs:
- Sergio Martin del Campo Barraza
External link. (now adjunct senior lecturer in our group), unsupervised machine learning for automation of wind turbine condition monitoring system, two years funded by SKF (2018-2019).
Highlights
- GPTech
External link. industrial chatbot for intelligent fault diagnosis under development by Karl Löwenmark
External link. (proposal written 2018 in collaboration with Stephan Schnabel
External link. at SKF, ahead of its time).
- Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems (2023), first PhD thesis
External link. at LTU focusing on neuromorphic computing. Mattias is a postdoc at Zenke Lab
External link..
- Machine Learning Concepts for Service Data Interoperability (2022), PhD thesis
External link. in Arrowhead
External link. investigating machine-learning based interoperability approaches.
- Gunnar Öquist Fellowship award
External link. and grant from The Kempe Foundations
External link..
- Ubiquitous Cognitive Computing: A Vector Symbolic Approach (2014), first PhD thesis
External link. I had the honour to supervise. Blerim worked on neurosymbolic and hyperdimensional computing in the early days of interoperability research and is now senior lecturer at Lund University
External link.. His pioneering work inspired Evgeny
External link., Denis
External link. and later Pedro
External link. at LTU to work on hyperdimensional computing.
- Dark matter in neutron stars hypothesis with Paolo Ciarcelluti: Paper
External link., paper
External link., impact
External link.. We showed that hidden sector dark matter candidates can modify the structure of neutron stars
External link..
- ISSP award
External link. for an Original Work in Theoretical Physics (note who signed the award: Prof. 't Hooft
External link. and Prof. Zichichi
External link., and consider the level of the 1999 Nobel Prize
External link.).
- Preon star hypothesis. Paper
External link., paper
External link., observational predictions
External link., Nature news
External link., Phys. Rev. Focus
External link., New Scientist
External link..
- Affiliated with WASP
External link.. Member of ELLIS
External link. and MODE
External link..
Courses
Professional education (upskilling)
- Neuromorphic Computing
External link., a navigational guide in the form of a 40 hour self-study course. Can be combined with D7064E (below) for in-depth knowledge.
Regular courses, 7.5 Hp/ECTS
- Programming for Machine Learning (D0036E
External link.), introduction to Python and machine learning with scikit-learn.
- Neural Networks and Learning Machines (D7046E
External link.), introduction to artificial and spiking neural networks, entry point for D7064E (below) or/and Advanced Deep Learning (D7047E
External link.).
- Also available as a 3rd cycle course including an individual project (for Ph.D. students).
- Neuromorphic Computing (D7064E
External link.), deepening course focusing on spiking neural networks and applications/principles of neuromorphic hardware.
- Also available as a 3rd cycle course including an individual project (for Ph.D. students).
- Programming for Scientific Computing (D7066E
External link.), focuses on programming and software engineering concepts for scientific computing.
- Master thesis courses in Engineerng Physics and Electrical Engineering (X7010E
External link., X7011E
External link., X0003E
External link.).
Since 2014, I am program director of the "Teknisk fysik och elektroteknik External link." program at LTU (Civilingenjör, 300Hp), co-directed with Andreas Almqvist
External link.. We develop the program with, e.g., guidance from CDIO, an annual program workshop organized in June, student workshops, and study visits with the program council.
Lectures also in D0032 Introduction to AI External link. and D0028E Programming and Digitalization
External link.. Was examiner for E0003E Electric circuit theory
External link., D0011E Digital design
External link., D0017E Introduction to programming for engineers
External link.. 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).
Note on philosophy of teaching
The modern dogma of education, where intended learning outcomes guide the design of examination and time-aligned active learning/tutoring activities based on modularised digital content with exercises is quite effective. We use this approach systematically and strive to improve our methods. However, the main bottleneck in education and upskilling is inner motivation. Much like the central role of the observer has been mostly neglected in modern science and mathematics it appears to me like the educational system has focused disproportionately on quantifying learning instead of stimulating curiosity and individual development. The multicosts of multitasking External link. blends functional stupidity into a dangerous cocktail of discouragement and culture devastation. Motivation is key to entering the magic realms of STEAM (Science, Technology, Engineering, Arts, and Mathematics).
Background
MSc diploma work External link. in ATLAS at CERN (2001), starting with PhD in Physics
External link. (2007, LTU, Swedish Graduate School
External link. of Space Technology) focusing on exotic phases of matter in neutron stars
External link.. As a PhD student I received the “New-Talents” award
External link. for an original work in theoretical physics at the International School of Subnuclear Physics
External link. in Erice, for work done with Johan Hansson
External link.. My first postdoc was funded by FNRS and focused on computational and fundamental physics at IFPA
External link. in Belgium (2008-2009).
A growing interest for the computational 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 received a Gunnar Öquist Fellowship External link. by the Kempe Foundations in 2014, including mentoring by Gunnar and 3 MSEK for research. I served as a technical committee member of the SKF–LTU
External link. University Technology Center, as a research theme leader (2013-2018) in the Intelligent Industrial Processes area of excellence, and as a member of the Applied AI Innovation Hub
External link..
I also served in several faculty workgroups focusing on improving education processes.
I have acted as reviewer for top journals (like TNNLS, Neuromorphic Computing and Engineering, Physical Review, Frontiers in Neuroscience, Physical Review) and the European Commission; reviewer of fundamental science applications, e.g., for the Australian and Swedish Research Councils; reviewer of professor applications; reviewer of PhD theses; workshop organizer; program chair. I coordinated national and EU research proposals. Professional education include: Organisational health and leadership (2014), Previa AB; Intellectual property (2014), SKF European Patent Office; Docent qualification (2012), LTU; Media training (2011), Kalix Folkhögskola; Teaching and learning in higher education (2010), LTU; Project management (2009), Astrakan AB; Personal leadership (2006), Personal Management International (PMI).
Member of WASP External link. and ELLIS
External link. since 2022 and MODE
External link. since 2023.
Other
Older code and tools. Contact me if you are interested in code used in more recent articles that are not listed here or cannot be obtained due to broken links etc (refer to contact info above).
- 3FCS code, link
External 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.
External link.
- Femtolensing tool, link
External 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
External link.. Result of early work on representation learning for cognitive computing.
- CBVS code, link
External link.. Early work on cognitive computing, related publications here
External link. and here
External link..
- Dircheck, link
External link.. A useful tool for verification of file archives.
- Python code for distributed computing over ssh, link
External link..
- ASOUND Matlab plugin, link
External link..
- Online collaborative writing in Latex, link
External link.. Developed before the era of Sharelatex / Overleaf etc.
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