Department of Information Technology

Machine Learning

Facilitating research collaborations between divisions and between the department of Information technology and external parts.

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.

Contact persons

  • 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).
Updated  2018-01-22 11:06:33 by Juozas Vaicenavicius.