Research group

Machine Learning and Information Management Lab

There are two different aspects of BIG DATA among the challenging Vs. Veracity and Variety require sophisticated statistical analysis, including machine learning. While Volume and Velocity are impossible without efficient technical solutions. Our research interests are spread among these directions. Most of our experience comes from the field of Information Retrieval and Databases. Currently we are focused on the following topics:

Theoretical Machine Learning (ML):

  • Tree models, sequence analysis, ensembles
  • GPU enabled ML

Search Engines (SE) and Information Retrieval (IR):

  • ML for Ranking, SE User Behaviour Analysis, SE performance, SE evaluation
  • Storage and processing of scientific, graph, etc. data

Information Management:

  • Stream processing, declarative computation in BD environment
  • Efficient storage and index structures, e.g column-oriented DB
  • Optimization and execution of declarative queries and workflows
  • Holistic application, optimization and tuning
  • Data quality
  • Consistency and high reliability

Besides research projects, we deliver the special courses:

Students interested in research problems in the areas of our interest are welcome to join our lab. The best way to learn more about our research is to take our courses or attend open seminars (to be done). New projects are launched regularly, sometimes it is also possible to join an ongoing project or extend its scope. Please contact the project leader for information on a specific project.

All students willing to join our projects must be skilled in either statistics, or programming, preferably in both. The best successful candidates will be invited to join one of projects as regular team members.