Supervision

PhD projects

Daily Supervisor Co-supervisor

Roser Montanana Rosell (2020 –)
- New avenues for studies of ALS-pathophysiology: interneuron contribution to disease development and degeneration
- Publications: Allodi et al. (2020)
João Campagnolo (2022 –)
- Neural correlates of stress resilience

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

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)

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