Algorithmic Data Analysis

The mission of the Data Analysis Research Programme at the HIIT is to develop useful algorithmic data analysis methods for other sciences and for industry. The work involves both basic research in computer science and applied work on problems arising from applications.

Research challenges

• Example challenge 1: Learning Network Structures. Network-like structures are numerous in various domains including molecular processes, social interactions, and the Internet. New computational methods are needed for finding the structure of such networks and for understanding their dynamic behaviour.
• Example challenge 2: The Vocabulary, Grammar and History of Genomes. The genome codes information identifying the species and the individual. Computational techniques are needed for the description and the analysis of variation. Segmentation methods using recurrent sources can be used to find components with similar underlying structure; latent variable techniques for sequences can also be used.
• Example challenge 3: Computational Modelling of Ecosystems. The environment can be measured in many ways on different scales ranging from remote-sensing based satellite images of landscapes to chemical compositions of nutrients in individual plants. The complex interactions in both the spatial and temporal domains across different scales are largely unknown, and their importance is growing.
• Example challenge 4: Sensor and Context Data Management. To realize a vision of ubiquitous information processing, services and applications make use of a wide variety of context data, including sensor readings. The challenges are to efficiently gather sensor data, to perform context reasoning, and to take into consideration the resource constraints of the devices and the distributed nature of the environment.

Research groups

• Combinatorial Pattern Matching Professor Esko Ukkonen

• Data Mining Professor Aristides Gionis

• Discovery Professor Hannu Toivonen

• Genome-Scale Algorithms Professor Veli Mäkinen

• Kernel Machines, Pattern Analysis and Computational Biology Professor Juho Rousu

• Neuroinformatics Professor Aapo Hyvärinen

• New Paradigms in Computing Professor Petteri Kaski

• Parsimonious Modelling Chief Research Scientist Jaakko Hollmén

Programme management

• Programme Director: Professor Aristides Gionis

• Programme Management Group: Research group leaders (see above)