Work
I'm a research scientist at SINTEF Digital .
My interests lie within responsible and ethical use of Artificial Intelligence (AI), including trustworthy, explainable and green AI.
I work a great deal 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
Op-eds (kronikker )
Klimakostnaden for bruk av Chat GPT kan bli større enn nytteverdien ChatGPT krever moderering á la Wikipedia Menneskapt eller ikke – spiller det noen rolle? Er vi klare for metaverset?
Software projects
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, 2024Measuring 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 2023AutoConf: Automated Configuration of Unsupervised Learning Systems using Metamorphic Testing and Bayesian Optimization , presentation of conference paper, ASE 2023, September 14th, 2023The future of software engineering in the light of LLMs , May 2023Replay-Driven Continual Learning for the Industrial Internet of Things , presentation of conference article, CAIN 2023, 15th May, 2023Machine Learning for Fatigue Detection using Fitbit Fitness Trackers , presentation of conference article, icSports 2023, 27th October, 2022UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing , presentation of conference article, CAIN 2022, 17th May, 2022
Scientific publications
26. 3D-DaVa: Enhancing 3D Point Cloud Data Reliability for Industrial Applications
Adela Nedisan Videsjorden, Arda Goknil, Sagar Sen, Erik Johannes Husom , Phu Nguyen
ACM Journal of Data and Information Quality. 2025-03-18.
25. ERG-AI: enhancing occupational ergonomics with uncertainty-aware ML and LLM feedback
Sagar Sen, Victor Gonzalez, Erik Johannes Husom, Simeon Tverdal, Shukun Tokas, Svein O Tjøsvoll
Applied Intelligence Volum 54 Issue 23. 2024-12.
24. Data quality as a service framework for AI-enabled industrial internet of things
Arda Goknil, Phu Nguyen, Erik Johannes Husom, Sagar Sen, Simeon Tverdal, Flavien Peysson, Dimitra Politaki, Roberto González-Velázquez
Proceedings of the 14th International Conference on the Internet of Things. 2024-11-19.
23. Drowsiness detection using federated learning: lessons learnt from dealing with non-IID data
Rustem Dautov, Erik Johannes Husom
Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments. 2024-06-26.
22. Latency-Aware Node Selection in Federated Learning
Rustem Dautov, Erik Johannes Husom
2024 IFIP Networking Conference (IFIP Networking). 2024-06-03.
21. Intend: Intent-based data operation in the computing continuum
Donatella Firmani, Francesco Leotta, Jerin George Mathew, Jacopo Rossi, Lorenzo Balzotti, Hui Song, Dumitru Roman, Rustem Dautov, Erik Johannes Husom , Sagar Sen, Vilija Balionyte-Merle, Andrea Morichetta, Schahram Dustdar, Thijs Metsch, Valerio Frascolla, Ahmed Khalid, Giada Landi, Juan Brenes, Ioan Toma, Róbert Szabó, Christian Schaefer, Cosmin Udroiu, Alexandre Ulisses, Verena Pietsch, Sigmund Akselsen, Arne Munch-Ellingsen, Irena Pavlova, Hong-Gee Kim, Changsoo Kim, Bob Allen, Sunwoo Kim, Eberechukwu Paulson
CEUR Workshop Proceedings, Volume 3692. 2024.
20. 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.
19. 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 .
18. Edge-based Data Profiling and Repair as a Service for IoT
Simeon Tverdal, Arda Goknil, Phu Nguyen, Erik Johannes Husom , Sagar Sen, Jan Ruh, Francesca Flamigni
13th International Conference on the Internet of Things. 2023-11-07.
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.
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