Work

I'm a research scientist at SINTEF Digital. My research interests lie within responsible and ethical use of Artificial Intelligence (AI), including trustworthy AI, explainable AI and green AI. I work a lot with AI engineering and applied machine learning.

I'm a part of the Trustworthy Green IoT Software research group in the Department of Sustainable Communication Technologies.

For professional inquiries, I can be contacted at erik.johannes.husom@sintef.no.

Active research areas and projects:

• Green AI: How can we make AI more energy-efficient and decrease the carbon footprint of generative AI? Project: ENFIELD.
• Agentic AI: How can we construct reliable and trustworthy systems of AI agents? Project: INTEND.

Quicklinks:

Media | Op-eds (kronikker) | Software projects | Talks/presentations | Publications

Media

• Abels Tårn: Forskningsfronten (NRK). Intervju hos det populærvitenskapelige radioprogrammet Abels Tårn hos NRK, 6. feb 2025.
• What Does OpenAI’s New Breakthrough Mean for Energy Consumption?. Interview with Heatmap News, Sep 17th, 2024.

Op-eds (kronikker)

• Klimakostnaden for bruk av Chat GPT kan bli større enn nytteverdien. Erik Johannes Husom, Finn Lützow-Holm Myrstad (fagdirektør i Forbrukerrådet), Gencer Erdogan (fungerende forskningsleder i Sintef Digital) og Elin Volder Rutle (leder for bærekraft i Forbrukerrådet). digi.no, 28. okt, 2024
• ChatGPT krever moderering á la Wikipedia. Rustem Dautov, Erik Johannes Husom. Teknisk Ukeblad, 16. mai, 2023
• Menneskapt eller ikke – spiller det noen rolle? Erik Johannes Husom. Vårt Land, 18. februar, 2023
• Er vi klare for metaverset? Erik Johannes Husom, Ketil Stølen. Klassekampen, 31. oktober, 2022

Software projects

• MELODI (Website) (Source code). Monitoring energy consumption of LLM inference.
• d2m (Website) (Source code). A machine learning pipeline for time series data, enabling responsible use of artificial intelligence.
• UDAVA (Website) (Source code). Unsupervised data validation.
• Green AI (Website) (Source code). A curated overview of Green AI resources.

Talks and presentations

• KI og bærekraft i et klimaperspektiv, innlegg hos NIBIO, 2. desember 2024.
• Hvordan få til bærekraftig bruk av KI?, foredrag på halvdagskonferasen "Bærekraftig bruk av AI" av Den norske dataforening, 14. nov 2024.
• KI og ressursforbruk fra et forbrukerperspektiv, innlegg hos Forbrukerrådet, 03. september 2024.
• Grønn KI, på arrangementet KI som klimaversting eller reddende engel? under Arendalsuka, 12. august 2024.
• Kunstig intelligens – bare et gode? Etiske utfordringer i møte med ny teknologi, 30. mai 2024.
• Engineering Carbon Emissions Aware Machine Learning Pipelines, presentation of conference paper, CAIN 2024, April 15th, 2024
• Measuring and understanding energy use in Large Language Model inference, 21. mars 2024.
• Kunstig intelligens og etikk, 6. mars 2024.
• Kunstig intelligens – pålitelighet og bærekraft, 30. januar 2024.
• Maskinlæringens klimaavtrykk: Hvordan måles det og hva kan vi bruke målingene til?, foredrag på seminaret "Bærekraft og maskinlæring – Lar det seg forene?", 21. september 2023
• AutoConf: Automated Configuration of Unsupervised Learning Systems using Metamorphic Testing and Bayesian Optimization, presentation of conference paper, ASE 2023, September 14th, 2023
• The future of software engineering in the light of LLMs, May 2023
• Replay-Driven Continual Learning for the Industrial Internet of Things, presentation of conference article, CAIN 2023, 15th May, 2023
• Machine Learning for Fatigue Detection using Fitbit Fitness Trackers , presentation of conference article, icSports 2023, 27th October, 2022
• UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing, presentation of conference article, CAIN 2022, 17th May, 2022

Scientific publications

• 19. Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning.
  • Rustem Dautov, Erik Johannes Husom.
  • 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 2024-04-16.
• 18. Engineering Carbon Emissions Aware Machine Learning Pipelines.
  • Erik Johannes Husom, Sagar Sen, Arda Goknil.
  • 2024 IEEE/ACM 3rd International Conference on AI Engineering–Software Engineering for AI (CAIN). 2024-04-15.
  • Link to presentation slides.
• 17. Automated Behavior Labeling for IIoT Data.
  • Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Sagar Sen, Phu Nguyen.
  • The 13th International Conference on the Internet of Things (IoT 2023). 2023-11-07.
• 16. REPTILE: a Tool for Replay-driven Continual Learning in IIoT.
  • Erik Johannes Husom, Sagar Sen, Arda Goknil, Simeon Tverdal, Phu Nguyen.
  • The 13th International Conference on the Internet of Things (IoT 2023). 2023-11-07.
• 15. Towards Community-Driven Generative AI.
  • Rustem Dautov, Erik Johannes Husom, Sagar Sen, Hui Song.
  • Position Papers of the 18th Conference on Computer Science and Intelligence Systems (fedCSIS 2023). 2023-09-17.
• 14. AutoConf: Automated Configuration of Unsupervised Learning Systems Using Metamorphic Testing and Bayesian Optimization.
  • Lwin Khin Shar, Arda Goknil, Erik Johannes Husom, Sagar Sen, Yan Naing Tun, Kisub Kim
  • 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023). 2023-09-14.
• 13. Uncertainty-aware Virtual Sensors for Cyber-Physical Systems.
  • Sagar Sen, Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Phu Nguyen.
  • IEEE Software. 2023-08-21. Journal paper.
• 12. Replay-driven continual learning for the industrial internet of things.
  • Sagar Sen, Simon Myklebust Nielsen, Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Leonardo Sastoque Pinilla.
  • 2023 IEEE/ACM 2nd International Conference on AI Engineering--Software Engineering for AI (CAIN). 2023-05-15.
  • Link to presentation slides.
• 11. Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline.
  • Sagar Sen, Erik Johannes Husom, Arda Goknil, Dimitra Politaki, Simeon Tverdal, Phu Nguyen, Nicolas Jourdan.
  • Computers in Industry, Volume 149, August 2023, 103917. 2023-08-01. Journal paper.
• 10. A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing.
  • Mauro Isaja, Phu Nguyen, Arda Goknil, Sagar Sen, Erik Johannes Husom, Simeon Tverdal, Abhilash Anand, Yunman Jiang, Karl John Pedersen, Per Myrseth, Jørgen Stang, Harris Niavis, Simon Pfeifhofer, Patrick Lamplmair.
  • Computers in Industry, Volume 146, April 2023, 103853. 2023-04-01. Journal paper.
• 9. Bridging the Gap Between Java and Python in Mobile Software Development to Enable MLOps.
  • Rustem Dautov, Erik Johannes Husom, Fotis Gonidis, Spyridon Papatzelos, Nikolaos Malamas.
  • 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). 2022-11-15.
• 8. Machine Learning for Fatigue Detection using Fitbit Fitness Trackers.
  • Erik Johannes Husom, Rustem Dautov, Adela-Aniela Nedisan, Fotis Gonidis, Spyridon Papatzelos, Nikolaos Malamas.
  • icSports 2022. 2022-10-28.
  • Link to presentation slides.
• 7. Taming Data Quality in AI-Enabled Industrial Internet of Things.
  • Sagar Sen, Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Phu Nguyen, Iker Mancisidor.
  • IEEE Software. 2022-08-01. Journal paper.
• 6. Towards MLOps in Mobile Development with a Plug-in Architecture for Data Analytics. 
  • Rustem Dautov, Erik Johannes Husom, Fotis Gonidis.
  • 2022 6th International Conference on Computer, Software and Modeling (ICCSM). 2022-07-01.
• 5. UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing.
  • Erik Johannes Husom, Simeon Tverdal, Arda Goknil, Sagar Sen.
  • 2022 IEEE/ACM 1st International Conference on AI Engineering--Software Engineering for AI (CAIN). 2022-05-16.
  • Link to presentation slides.
• 4. Deep learning to predict power output from respiratory inductive plethysmography data.
  • Erik Johannes Husom, Pierre Bernabé, Sagar Sen.
  • Applied AI Letters. 2022-04. Journal paper.
• 3. On The Reliability Of Machine Learning Applications In Manufacturing Environments.
  • Nicolas Jourdan, Sagar Sen, Erik Johannes Husom, Enrique Garcia-Ceja, Tobias Biegel, Joachim Metternich.
  • Workshop on Distribution Shifts, 35th Conference on Neural Information Processing Systems (NeurIPS 2021). 2021-12-19.
• 2. Deep learning to estimate power output from breathing.
  • Erik Johannes Husom.
  • 2021-06. MSc Thesis.
• 1. DeepVentilation: Learning to Predict Physical Effort from Breathing.
  • Sagar Sen, Pierre Bernabé, Erik Johannes B. L. G. Husom.
  • Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. 2020-07.