There are many different aspects to BIG DATA whether challenging Veracity or Variety they all require sophisticated statistical analysis, provided by 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.
Main research projects
Theoretical Machine Learning (ML)
Tree models, sequence analysis, ensembles, and GPU enabled ML
Search Engines (SE) and Information Retrieval (IR)
ML for Ranking, SE User Behaviour Analysis, SE performance, SE evaluation and Storage and processing of scientific and graph data
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. The holistic application, optimization, and tuning. Data quality. Consistency and high reliability.
Beside the research projects, we deliver special courses:
- Machine Learning @Computer Science Centre (https://compscicenter.ru/courses/machine-learning-1/2016-autumn/, https://compscicenter.ru/courses/machine-learning-2/2017-spring/)
- Search Engine Architecture @St.Petersburg State University
- Information Management @St.Petersburg State University
- Database algorithms @St.Petersburg State University
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 our open seminars. 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 successful candidates will be invited to join one of the projects as a regular team member.
- Public speaking master class
- PolyTree framework for tree ensemble analysis
- Aggregation of pairwise comparisons with reduction of biases
In Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems (DEBS '19). ACM, New York, NY, USA, 264-265.
The 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), October 22–26, 2018, Torino, Italy. ACM, New York, NY, USA, 10 pages
Message from the editorsCEUR Workshop Proceedings, 1864,