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
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
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