Algorithmics and Computational Intelligence Group (ACI)
The research of the laboratory is centered around techniques and methods for algorithm design and computational intelligence, with the emphasis on both theory and applications. The foundations of the research are machine learning, probabilistic inference, discrete mathematics and theoretical computer science. In particular, the research of kernel methods, Bayesian analysis, probabilistic and information-theoretical modeling, combinatorial algorithms and intelligent systems has been pursued. The research of the laboratory is based on the long tradition of combining basic research on algorithm development and analysis with active cooperation with companies and academic partners on solving real-life problems by the use of combinatorial optimization and latest techniques on computational intelligence methods.
Selected research topics on algorithmics and computational intelligence:
- Classification and regression methods
- Clustering methods
- Combinatorial algorithms and applications
- Cross-validation methods
- Data compression
- Feature selection methods for high-dimensional data
- Industrial algorithms
- Information retrieval
- Information theoretic methods
- Multi-task and transfer learning
- Preference learning and ranking
- Probabilistic Bayesian methods
- String algorithms
- Tensor product kernels for pairwise learning
- Experiences from a development project in Kenya – Baselines for future climate information systems
Helminen J., Kirongo B., Gaiani S., Misaki E., Apiola M., Sutinen E.
- Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model
Veerasamy Ashok Kumar, Daryl D’Souza, Rolf Lindén, Mikko-Jussi Laakso
- Rolling horizon production scheduling of multi-model PCBs for several assembly lines
Koskinen J., Raduly-Baka C., Johnsson M., Nevalainen O.
- IMPROD biparametric MRI in men with a clinical suspicion of prostate cancer (IMPROD Trial): Sensitivity for prostate cancer detection in correlation with whole-mount prostatectomy sections and implications for focal therapy
Merisaari H., Jambor I., Ettala O., Boström P.J., Montoya Perez I., Verho J., Kiviniemi A., Syvänen K., Kähkönen E., Eklund L., Pahikkala T., Vainio P., Saunavaara J., Aronen H.J., Taimen P.
- Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health
Azimi I., Pahikkala T., Rahmani A., Niela-Vilén H., Axelin A., Liljeberg P.
- Lecture Notes on Interactive Storytelling
Jouni Smed, Tomi Suovuo, Natasha Trygg, Petter Skult
- Pain process of patients with cardiac surgery — Semantic annotation of electronic patient record data
Heikkilä Kristiina, Axelin Anna, Peltonen Laura-Maria, Heimonen Juho, Anttila Pauliina, Viljanen Timo, Salakoski Tapio, Salanterä Sanna
- Optimized reference spectrum for rating the impact sound insulation of concrete floors
Kylliäinen M., Virjonen P., Hongisto V.
- A survey on the use of data points in IDS research
Heini Ahde, Sampsa Rauti, Ville Leppänen
- Luminometric label array for quantification of metal ions in drinking water – Comparison to human taste panel
Tanja Seppälä, Tuuli Ruponen, Mari Sandell, Parisa Movahedi, Ileana Montoya Perez, Leo Lepistö, Oskar Laaksonen, Tapio Pahikkala, Harri Härmä, Sari Pihlasalo