DataAI group has a long history in data analysis research. The application areas have varied from steel manufacturing and spot welding to human activity recognition from e.g. accelerometer data, and many others. Currently DataAI is taking steps to broaden its research area to include big data related problems as we see huge potential in this area. The potential is also recognized in reports that try to forecast the future key technologies. See more under Research Highlights section.
In the research on industrial statistics, data analysis approaches are developed for optimization, control and planning of industrial processes. The scope of applications has varied from spot welding to cheese manufacturing, and much of the research has focused on improving product quality in steel industry. See more under Research Highlights section.
Machine Learning and Knowledge Discovery
Machine learning as a sub-field of artificial intelligence studies methods that can automatically detect patterns in data by optimizing the performance criteria from the examples or past experiments. A learned model can be used to predict the response variables or other properties of unknown patters. In our research, we are concentraiting on the development of statistical (and probabilistic) learning methods which are able to tackle the different uncertainties and non-linear dependencies typically involved in the patterns and large-scale datasets. Furthermore, we are interested in particular algorithms which are able to utilize multi-modal and structured data in different data analysis and prediction scenarios such as classification, regression, clustering, and sequential modeling, using tools such as instance-based learners, neural networks, kernel methods, Bayesian non-parametric methods, and Bayesian filtering. The scope of our research is mainly on the application side of statistical machine learning in the areas of industrial manufacturing processes, context-aware computing, human-computer interaction, data mining, and robotics. On the other hand, when applying machine learning in practice, they require the support of an infrastructure that provides them with efficient access to data. Thus also research on information storage focusing on developing novel data structures and access interfaces to support various analysis and inference tasks is a key element in our group. A special case of this is the work on knowledge retrieval, in which the stored information is enriched with semantic metadata that allows its real-world significance to be parsed by automated search algorithms. Besides supporting other areas of research, this area has independent applications e.g. in the field of intelligent manufacturing. See more under Research Highlights section.
Last updated: 11.7.2016