I supervise theses on topics ranging from topological machine learning and geometric deep learning to machine learning applications in medicine and energy. Below you find a list of current and former students I worked with and their thesis titles. You find some of the master thesis projects that I am interested in supervising here. If any of them interests you or you have your own project idea related to my research interests, I am happy to talk to you.

PhD students

Ongoing

• Preben Johnsen Bentdal (2025-, graph neural networks)
• Sophie Blum (2025-, energy informatics, co-supervisor)
• Heebah Saleem (2024-, generative modelling, co-supervisor)
• Morten Blørstad (2023-, uncertainty in ML, co-supervisor)
• Francesco Ballerin (2022-, geometric deep learning, co-supervisor)
• Odin Hoff Gardå (2022-, topological data analysis)
• Natacha Galmiche (2019-, multimodality in meteorology)

Graduated

• Emmanuel Sam (2025). Computing Optimal Lineal Topologies (DFS Trees): A Parameterized Complexity Perspective.
• Erlend Raa Vågset (2024). Optimal Parameterized Algorithms for Solving NP-Hard Problems in Topology.
• Lars Moberg Salbu (2024). Simplicial Constructions in Applied Topology (co-supervisor).

Master students

Ongoing

• Carina Elisabeth Seidel
• Eirik Pedersen
• Julian Horn
• Ole Straumland
• Maya Sommerro Evensen
• Hevar Mokhlis
• Magnus Fjeldstad
• Tobias Husebø
• Elias Hovdenes
• Tonje Mjaaland
• Astrid Hillesøy
• Alexander Akhtar
• Bjørn Kyvik Høivangli
• Sander Braastad
• William Wale

Graduated

• Tobias Soltvedt (2025). Topology of Flight Paths.
• Nikolaus David Engh (2025). Deep Learning for Peripherin Prediction in Breast Cancer Imaging Mass Cytometry.
• August Tidemann Klevberg (2025). Advancements in Machine Learning for Second Language Acquisition: A Comparative Analysis Using Duolingo’s 2018 Shared Task Dataset.
• Alina Artemiuk (2025). Neural Structures in Breast Cancer: Segmentation and Survival Outcomes.
• Mathilde Pytte Nordli (2025). Machine learning for single-cell immune profiling in MS after stem cell transplantation.
• Vegard Rist Sørheim (2025). ECD Forecasting Using LSTM with Physics-Based Simulated Time Series Data in a Planning Regime.
• Sondre Bergsvåg Risanger (2024). A Benchmarking Suite for Persistent Homology.
• Leander Jacob Nielsen Eikås (2024). Machine learning for early detection of structure loss in the Czochralski process
• Edvards Zakovskis (2024). Multitask variational autoencoders.
• Jonatan Berg Romundgard (2024). Enhancing weather forecasts using deep learning methods.
• Willem Theodorus Schooltink (2023). Topological Regularization of Support Vector Machines.
• Sigurd Roll Solberg (2023). Vectorizing Distributed Homology with Deep Set of Set Networks.
• Audun Ljone Henriksen (2023). Learning Acquisition Functions for Cost-aware Bayesian Optimization.
• Simen Høyvik (2023). Document Ranking for Systematic Reviews in Medicine.
• Thorarinn Sigurvin Gunnarsson (2023). Metrics exploration in the context of ensemble weather forecasts using deep learning.
• Morten Blørstad (2023). Improving Stability of Tree-Based Models.
• Julia Cat-Vy Nguyen (2023). Vessel recognition in ultrasound images using machine learning techniques.
• Adrian Tvilde Evensen (2022). Binary domain classification for Norwegian language in task-oriented dialogue systems.
• Brage Alvsvåg (2022). Improving fish detection using efficient neural networks.
• Fromsa Hera (2022). Density estimation of single-cell mass cytometry with generative models.
• Jonas Folkvord Triki (2021). Analysis of Word Embeddings: A Clustering and Topological Approach.
• Benjamin Mekki Widerøe (2019). Forecasting Electricity Production from Photovoltaic Solar Panels using Elastic Net Regularization.
• Karoline Lekve (2019). Forecasting Variables for Solar Panel Energy Production using Historical Data.
• Tam Thanh Truong (2017). Persistent Homology via Quotient Spaces.