Student Projects

The research in the ADA group is focused on three related areas:

• Data Mining,
• Bioinformatics, and
• Database Systems

Doing a research project in our group is an excellent opportunity to acquaint yourself both with doing research and the topics under investigation in our group. As an exhaustive list of all possible projects would be impossible, we only provide detailed descriptions of a couple of projects. Also, examples of previous projects are presented to provide you a better view of what we'd expect from your work. We warmly welcome you to come up with ideas or topics to work on.

Our group's research is positioned within the Applied Computing Sciences (ACS) cluster, of which Ad Feelders is the student advisor. So, if you are interested in doing either an Experimentation Project or your MSc thesis on a subject in one of our research areas, contact one of the ADA members or send an e-mail to Ad Feelders.

Master Thesis Projects

Below is a non-exhaustive list of currently open thesis projects. Of course, we also encourage you to come up with your own ideas for a thesis project in one of our research areas.

• Data Mining with Monotonicity Constraints
• Multi-relational Data Mining

To provide you a better view of what a thesis project at our group would consist of, we present some finished projects.

• RMDB: A Relational Algebra for Monet
• Decoding Spatiotemporal fMRI Activation Patterns via Kernels
• Building a Global Model from Local Patterns by Pattern Team Discovery
• ALIRA: An (Almost) Lazy Information Retrieval Application
• Database support voor genoomstrings

Experimentation Projects

Below is a non-exhaustive list of currently open experimentation projects.

• Database Characterisation by Visualisation
• Comparative study of sequence alignment methods on Monet
• Mining Mice

As inspiration, and insight of what to expect from an experimentation project at our group, we provide a list of finished experimentation projects.

• Discriminative Learning of Bayesian Network Classifiers
• Learning Distance Functions for KNN Classifiers