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Research profile

My main academic interests lie at the intersection of machine learning and remote sensing, where I develop methods to analyze and interpret Earth observation data.

I have a particular focus on applications in the cryosphere, where I so far have been working with the recovery of sea ice concentration estimations from historical passive microwave data, and work with automatic ice charting using Sentinel-1 imagery.

I am currently working with self-supervised learning, where I train foundation models from vast amounts of unlabeled satellite imagery, particularly from synthetic aperture radar (SAR), though not limited to it. I will train models for applications in sea ice domains, as well as other environmental tasks within the danish realm.

Education/Academic qualification

Mathematical modelling and computation, MSc., Evaluating Masked Autoencoders as a Method for Label- Efficient Automated Sea Ice Concentration Mapping

1 Sept 202330 Jun 2025

Award Date: 30 Jun 2025

Earth and Space Physics and Engineering, BSc, The Nimbus 6 Electrically Scanning Microwave Radiometer: data rescue, Technical University of Denmark

1 Sept 202030 Jun 2023

Award Date: 30 Jun 2023

Keywords

  • Geography and Geology
  • Remote sensing
  • SAR
  • Arctic
  • Sea Ice
  • Flooding
  • Mathematics and Statistics
  • Deep Learning
  • Foundation Models
  • Self-Supervised Learning
  • Computer Vision

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  2. SDG 13 - Climate Action
    SDG 13 Climate Action
  3. SDG 14 - Life Below Water
    SDG 14 Life Below Water
  4. SDG 15 - Life on Land
    SDG 15 Life on Land

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