Steffen Mæland
Field of work
Particle physicist, doing research on data from the ATLAS experiment at CERN. Interested in the CP-properties of the Higgs boson, both in production and decay.
Focusing in particular on machine learning methods for reconstruction, for calibration, and for searches for new physics. Has also worked on machine learning for a series of other applications.
Interested in master projects?
The ATLAS group at HVL offer several cool projects in machine learning, software, and computing. Some examples of past and present master theses within the group:
- Anomaly Detection in Search for New Physics Beyond the Standard Model with Graph Autoencoders, T Thunes
- Simulation and analysis of particle beam interactions with semiconductor sensors, P K Ofstad
- Tau lepton classification with graph neural networks, T S Kristensen
- Assessing model robustness and performance through noise: A case study using data from the ATLAS experiment, I Foster
- Using graph neural networks in high energy physics data analysis, Ø Vikane
- Specially designed random forest loss function for high energy physics, D Sprindys
- Interpretable machine learning and feature selection in the search for dark matter, Ø J Birkeland
- Applied machine learning on ATLAS data in search for supersymmetry, C Steinfinsbø
Future projects could be development for new machine learning methods, preferable neural networks; analysis of and improvement of robustness, interpretability and explainability of machine learning models; automating data analyses; anomaly detection, or other related topics. A research stay at CERN can be part of the project.
Courses taught
Physics, mathematics, machine learning.
Publications
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Towards integrating of machine learning into array processing pipelines
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Introduksjon til partikkelfysikk
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Hva er KI?
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Goals for HL-LHC and how to meet them
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Update on MDN for TES