Master Thesis Projects
Several master thesis projects in machine learning are supervised at our department each year. If you are interested in pursuing such a project, feel free to contact us. Below we list a few contact persons and their ML-related research interests.
- Kristiaan Pelckmans: novelty detection, recommender systems, online machine learning, reinforcement learning, assisted daily living.
- Dave Zachariah: statistical machine learning, sparse models, online learning, dynamical systems.
- Anders Hast: handwritten text recognition, word spotting, (semi)automatic transcription.
- Olle Gällmo: neural networks, reinforcement learning, genetic algorithms, swarm intelligence.
- Fredrik Lindsten: statistical machine learning, Bayesian inference, dynamical systems, Monte Carlo methods.
- Lawrence Murray: Bayesian methods, Monte Carlo methods, probabilistic programming, application areas (e.g. epidemiology, phylogenetics, geophysics, ecology).
- Jalil Taghia: Approximate methods in Bayesian inference, variational inference, latent variable models, directional statistics, brain functional connectivity analysis).
- Niklas Wahlström: statistical machine learning, dynamical systems, deep learning, sensor fusion
- Juozas Vaicenavicius: deep learning, probabilistic modelling, Bayesian methods, statistical decision theory, applications (e.g. autonomous driving, finance).