Supervision

PhD projects

Daily Supervisor

• Jákup O Svöðstein (2023–)
  • Topic: Data-Driven Machine Learning Approaches for Compressible and Incompressible Fluid Dynamics Modelling
  • Co-supervisors: Erik B Dam, Knud Simonsen
• Frederik L Johansen (2022–)
  • Topic: Generative learning for Inorganic Material Science
  • Co-supervisors: Erik B Dam, Kirsten MØ Jensen
  • Publications:
    • Frederik Lizak Johansen, Andy Sode Anker, Ulrik Friis-Jensen, Erik Bjørnager Dam, Kirsten Marie Ørnsbjerg Jensen, Raghavendra Selvan. A GPU-Accelerated Open-Source Python Package for Calculating Powder Diffraction, Small-Angle-, and Total Scattering with the Debye Scattering Equation. Journal of Open Source Software (JOSS), 2024.
    • Ulrik Friis-Jensen, Frederik Lizak Johansen, Andy Sode Anker, Erik Bjørnager Dam, Kirsten Marie Ørnsbjerg Jensen, Raghavendra Selvan. CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning. Arxiv, 2024.

Co-supervisor

• Anna Stuckert (2023–)
  • Topic: Facing Dementia: Investigating disease onset in a mouse model of Frontotemporal Dementia via Artificial Intelligence
  • Co-supervised with Ilary Allodi
• João Campagnolo (2022 –)
  • Topic: Neural correlates of stress resilience
  • Co-supervised with Florence Kermen
• Ulrik Friis-Jensen (2022–)
  • Topic: Analysis of Total Scattering Data using Generative Machine Learning Models
  • Co-supervised with Kirsten MØ Jensen
• Roser Montanana Rosell (2020 – 2023)
  • Topic: New avenues for studies of ALS-pathophysiology: interneuron contribution to disease development and degeneration
  • Co-supervised with Ole Kiehn and Ilary Allodi
  • Publications: Montanana-Rosell et al. 2023; Allodi et al. (2020)

Masters Theses

Primary supervisor

• Justinas Antanavicius (2021), Registering Mouse Brain Slices to a Reference Atlas with Convolutional Neural Networks (publication)
• Jan Mikolaj Kaminski (2020), Deep learning-based spatial localisation of interneuron markers within the spinal cord by in situ sequencing (publication)
• Anand Bansal (2020), Uncertainty quantification in medical image segmentation
• Ruta Masiulyte (2019), Retinal blood vessel segmentation using GNNs
• Basile Nicolas Rommes (2019), Mean Field Networks for Retinal Blood Vessel Segmentation

Co-supervisor

• Frederik L. Johansen (2022), Developing a Deep Q-Learning Framework for Atomic Structure Determination of Mono-Metallic Nanoclusters using Pair Distribution Function Data
• Ulrik Friis-Jensen (2022), Using Deep Generative Models for Atomic Structure Solution of Metal Oxide Nanoparticles from Pair Distribution Function Data
• Abraham Smith (2018), Root Segmentation using Convolutional Neural Networks (publication)

Bachelor projects

Primary Supervisor

• Pedram Bakhtiarifard (2022), Carbon Aware Tabular Benchmarks for Neural Architecture Search
• Nicklas Boserup (2022), Self-supervised image segmentation using contrastive regions
• Björn Wadmark (2022), Spike Sorting of Neuronal Activity Obtained From Brains of Mice
• Jakob Flinck Sheye (2022), Influence of Local Feature Maps in Matrix Product State Tensor Networks
• Björn Olof Christian Wadmark (2022), Spike Sorting of Neuronal Activity Obtained From Brains of Mice
• Kevin Weng (2021), Self-supervised learning for medical image segmentation in high-resolution microscopy images
• Kasper Munk Rasmussen (2021), Unsupervised learning of objects and concepts with a focus on medical images
• Christoffer Winther (2021), Similarity measures of nanomolecules using self-supervised graph neural networks
• Benjamin Kanding & Lasse F. Wolff Anthony (2020), The Carbon Footprint of Training Deep Learning Models, (Python package, Publication)
• Gilli Fjallstein (2020), Locomotion behaviour analysis of mice from video sequences

Co-supervisor

• Peter Kristoffer Licht (2016), Implementation of automatic blood vessel segmentation in retinal images using the Kalman filter

Course Projects

Primary Supervisor

• Søren Alexander Flensborg (2021), BSc project, 3D image segmentation using strided tensor networks (publication)
• Justinas Anatanavicius (2021), MSc project, Identifying 2D brain slices in a 3D reference atlas using Siamese Networks (publication)
• Klas Rydhmer (2021), PhD project, Dynamic β-VAEs for quantifying biodiversity by clustering optically recorded insect signals (publication)
• Andy Sode Anker (2020), MSc project, Using CVAEs to Extract Structural Motifs from X-ray Scattering Data (publication)

Co-supervisor

• Julian Elisha Schön (2022), MSc project, Temporal Embeddings in Deep Generative Latent Models for Disease Prediction and Treatment Planning
• Manh Cuong Ngo (2020), PhD project, Detection of foraging behavior from accelerometer data using U-Net type convolutional networks (publication)
• Xuan Zhong (2019), BSc project, Zero-Shot Relation Extraction using Graph Neural Networks