Possible Thesis Topics with Oliver Schulte (February 2017)

I am researching machine learning for structured data, SQL, XML, network data, event logs, sports data.

Basic Research.

Some of the basic research question for such data include the following.
  • Learning Bayesian networks. We use these to model the joint distribution of object attributes and links, and for feature generation.
  • Classification.
  • Anomaly Detection and Exception Mining.
My group has developed novel methods for these problems that work well already. Scaling to big data with millions of datapoint is a particular strength of our methods.
Scaling to hundreds of features (attributes and relationship types) is one of the research topics I would like to work on. This combines systems tools (e.g. Spark, Hadoop) with machine learning.

Application Areas

Relational learning has many exciting application areas. I am especially interested in the following.
  • Statistical Modelling of Sports Data. E.g. player ranking, drafting decisions, match outcome prediction.
  • Detecting Relationships in Computer Vision.
  • Extracting Relationships from Text.
  • Anomaly Detection, Data Cleaning.
  • Business Process Mining (see the BPM challenge ).
I'm also interested in challenge competitions for structured data like the Yelp Dataset Challenge.