Home » Research » Research programmes » Algorithmic Data Analysis

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 projects

Please see the web pages of the research groups below.

• Algorithmic and Probabilistic Methods in Data Mining, Heikki Mannila
  
• New Computational Methods for Analyzing the Structural and Functional Landscapes of Mammalian Genomes (CompGenome), Heikki Mannila
• Patterns and decompositions, Heikki Mannila
• Computational Methods for the Analysis of Palaentological data, Heikki Mannila
• Algorithmic pattern matching and data mining theory, Heikki Mannila
• Whole genome association analysis strategies for multiple phenotypes, Heikki Mannila
• Data mining tools for changing modalities of communication (Dammoc), Heikki Mannila
• Algorithmic Bayesian data analysis with applications in bioinformatics, Mikko Koivisto
• Algorithmic Data Analysis (Algodan) - Centre-of-Excellence, Esko Ukkonen, Heikki Mannila
• European network of genome annotation (Biosapiens), Esko Ukkonen
• Regulatory Genomics, Esko Ukkonen
• Virtual Intelligent Space for Collaborative Innovation (VISCI), Patrik Floréen
• Optimising Data Gathering in Resource-Constrained Networks (Geru), Patrik Floréen
• Personalised Ubiservices in Public Spaces (PUPS), Patrik Floréen
• Local and User-Created Services (LUCRE), as part of the Flexible Services programme of the ICT SHOK, Patrik Floréen
• Methods for Combinatorial Construction (MOCCA), Petteri Kaski
• Intelligent Structural Health Monitoring System (ISMO), Jaakko Hollmén
• Molecular markers for asbestos-exposure related lung cancer, Jaakko Hollmén
• Analysis of dependencies in environmental time-series (AD/ED), Jaakko Hollmén
• Computational data fusion of multiple biological information sources and background data (MULTIBIO), Samuel Kaski
• Computational translation from model organisms to humans (TRANSCENDO), Samuel Kaski
• Bisociation Networks for Creative Information Discovery (Bison), Hannu Toivonen
• Knowledge discovery in biological databases (Biomine), Hannu Toivonen
• Data mining in genetics, Heikki Mannila

Past research projects

• Genetic Analysis of Schizophrenia Phenotype (Orion), Heikki Mannila
• Combining multiple data sources in functional genomics for improving genome-wide inferences, Jaakko Hollmén
• Inductive Queries for Mining Patterns and Models (IQ), Heikki Mannila
• Semantic Interpreter Widened Experience (Stepwise), Patrik Floréen
• Data fusion in bioinformatics (MudFun), Samuel Kaski

Programme management

• Programme Director: Professor Sami Kaski
• Programme Manager: Dr Ella Bingham
• Programme Management Group
  • Professor Heikki Mannila
  • Dr Jaakko Hollmén
  • Professor Aapo Hyvärinen
  • Professor Samuel Kaski
  • Academy Fellow Veli Mäkinen
  • Professor Hannu Toivonen
  • Professor Esko Ukkonen

Research groups

• Combinatorial Pattern Matching, Professor Esko Ukkonen
• Data Mining: Theory and Applications, Professor Heikki Mannila
• Discovery Group, Professor Hannu Toivonen
• Neuroinformatics Professor Aapo Hyvärinen
• Parsimonious Modelling, Chief Research Scientist Jaakko Hollmén
• Statistical Machine Learning and Bioinformatics, Professor Samuel Kaski
• Succint Data Structures, Academy Fellow Veli Mäkinen