Research group:

We work on machine learning, signal processing and automatic control. Developing basic theory and creating new applications in medicine.

• Postdoc: Elis Stefansson. Complexity and robustness in learning and decision-making
• Postdoc: Petrus Abreu. Automatic ECG analysis and telehealth
• Ph.D. (main supervisor): Jiawei Li. Foundation models for electrocardiography
• Ph.D. (main supervisor): David Väviggreen. Robust methods for unsupervised machine learning
• Ph.D. (co-supervisor): Bror Hultberg. Anytime inference and decision making under uncertainty with application to precision medicine
• Visiting student: Vitoria Sitati. Interpretability and repeatability in AI-ECG diagnosis (CORE-AI program)

Alumni:

• Ph.D. (co-supervisor)
  • Daniel Gedon. On Deep Learning for Low Dimensional Representations (Defence: June 2024).
• M.Sc. (supervisor)
  • Wenhan Zhou. Conformal prediction for automatic electrocardiogram analysis (Defence: June 2025).
  • Arvid Eriksson. Adversarial training for the diagnosis of the ECG (Defence: June 2024).
  • Oscar Larsson. Generation and Detection of Adversarial Attacks in the Power Grid (Defence: July 2022).
  • Theogene Habineza. Deep Learning-Based Risk Prediction of Atrial Fibrillation Using the 12-lead ECG (Defence: June 2022).
• M.Sc. (subject reviewer)
  • Johan Millberg. Evaluating clusters of financial time series (Defence: June 2023).
  • Christie Courtnage. An extension to Semi-Supervised Learning using Shapley Value Data Valuation (Defence: June 2022).
  • Meenal Pathak. Automated Accounting using Machine Learning (Defence: Apr. 2022).
• Visiting students
  • Kellen Sumwiza. AI-ECG age in the prediction of cardiovascular diseases (CORE-AI program). Sep to Nov 2024 (University of Rwanda, Rwanda).
  • Lennert Bontick. AI-ECG invariant to patient Characteristics via Gradient-Reversed. Aug to Sep 2024 (Ghent University, Belgium).
  • Karel Fonteyn. Orthogonalized deep learning for ECG Risk Prediction. Aug to Sep 2024 (Ghent University, Belgium).