Stelios Sfakianakis received his BSc and MSc diplomas from the Department of Informatics and Telecommunications of the University of Athens and his Ph.D. from the School of Electrical and Computer Engineering of the Technical University of Crete. Since 2000 he has been with the Computational BioMedicine Laboratory of FORTH, working on integrating systems in the field of biomedicine, semantic interoperability, and building tools and services for intelligent data analysis. He has participated as work packages leader in numerous European research projects, focusing on the development of innovative ICT solutions to support large-scale transcription research on cancer, the discovery of biological cancer markers, transcription medicine, and the design and implementation of computational infrastructures for the integration of data and services. His research interests include biomedical computing, web-based cloud-based software architectures, and data mining and analysis with modern computing tools. He has published more than 60 articles in international journals and conference proceedings related to his areas of expertise.
His work mainly focuses on the implementation of health informatics applications and services using well known standards such as HL7 and IHE, the semantic integration and composition of services in state of the art computational environments such as the Grid and the Semantic Web, and the design and implementation of post-genomic data architectures and statistical analysis tools. In the past he has worked in the design and implementation of a service oriented architecture for the realization of the Integrated Electronic Patient Health Record by the means of CORBA (and TAO) and Web Services middleware technologies. Nowadays, his technical interests mostly centre on the use of modern web technologies (HTML5 umbrella of technologies and REST “inspired” integration approaches) and the use of machine learning and statistics for the analysis of “big data” (what is now frequently termed as “data science”).
- Health Informatics
- Machine Learning
- Functional Programming