Scholar
Tapio Pahikkala
Google Scholar ID: ROQQmPEAAAAJ
Professor, University of turku, Finland
Citations & Impact
Citations
4,400
H-index
33
i10-index
87
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Research Experience
- - EU-funded project: Privacy-preserving identity management for digital wallet and secure data sharing (PRIVIDEMA), starting November 2024
- - EU-funded project: EMotion Artificial Intelligence specialists for Europe (EMAI4EU)
- - EU-funded project: Phase IV AI: Privacy compliant health data as a service for AI development
- - Academy of Finland funded project: AI technologies for interaction prediction in biomedicine (AIB project, 2022.01.01 - 2024.12.31)
- - Academy of Finland funded project: Machine Learning for Systems Pharmacology (MASF project, 2021.09.01 - 2025.08.31)
- - Business Finland funded project: Privacy-preserving AI for Synthetic and Anonymous Health Data (Privasa)
- - Academy of Finland funded project: Tensor Learning for Biomedicine (TensorBiomed, 2018.01.01 - 2019.12.31)
- - Academy of Finland funded project: Tensor-Based Machine Learning for Big Data with Inherent Dependencies (TensorML, 2017.09.01 - 2021.08.31)
- - Centre for Economic Development, Transport and the Environment funded project: Identification of common wild oat and other weeds from drone photographs
- - Regional Council of Southwest Finland funded project: ICT education export, 2016.11.01 - 2017.10.31
- - Joint research project with TribeFlame Ltd.: Computational Analysis of Mobile Game Data, 2015.10.01 - 2016.09.30
- - Finnish Funding Agency for Technology and Innovation (Tekes) funded project: New computational methods for the effective utilization of public data, 2013.01.01 - 2014.12.31
- - Academy of Finland Postdoctoral researchers project: Machine Learning for Automated Solving of Multitask Problems, 2010-2012
Background
- Currently works as a professor of computer science at the Department of Computing, University of Turku, Finland. His research interests include the theory and algorithmics of machine learning and data analysis methods, their performance evaluation especially with resampling methods, and their applications in various different fields.
Co-authors