Tapio Pahikkala
Professor
aatapa@utu.fi +358 29 450 4323 +358 50 345 5824 Office: 456D ORCID identifier: https://orcid.org/0000-0003-4183-2455 |
Machine learning, Data science, Artificial intelligence
Machine learning, Data science, Artificial intelligence
Tapio Pahikkala is a professor of computer science in the University of Turku, Finland, from which he also received his doctoral degree in 2008. He has authored more than 150 peer-reviewed scientific articles and participated in the winning teams of several international scientific competitions/challenges. He has led many research projects, supervised more than ten doctoral theses, held several positions of trust in academia and served in the program committees of numerous international conferences. His current research interests include theory and algorithmics of machine learning, data analysis, and artificial intelligence, as well as their applications on various different fields.
Theory and algorithmics of machine learning, data science and artificial intelligence as well as their practical applications in various different fields. Estimation of prediction performance with resampling methods, theory of resampling and cross-validation.
TKO_7092 Evaluation of Machine Learning Methods
- Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques (2020)
- Journal of Magnetic Resonance Imaging
(A1 Refereed original research article in a scientific journal) - Synthetic minority oversampling of vital statistics data with generative adversarial networks (2020)
- Journal of the American Medical Informatics Association
(A1 Refereed original research article in a scientific journal) - Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology (2020)
- Forestry
(A1 Refereed original research article in a scientific journal) - Automatic detection of cereal rows by means of pattern recognition techniques (2019)
- Computers and Electronics in Agriculture
(A1 Refereed original research article in a scientific journal) - Energy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model (2019)
- IEEE Transactions on Cloud Computing
(A1 Refereed original research article in a scientific journal) - 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 (2019)
- Journal of Magnetic Resonance Imaging
(A1 Refereed original research article in a scientific journal) - Luminometric label array for quantification of metal ions in drinking water – Comparison to human taste panel (2019)
- Microchemical Journal
(A1 Refereed original research article in a scientific journal) - Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health (2019)
- Future Generation Computer Systems
(A1 Refereed original research article in a scientific journal) - Prebiopsy IMPROD Biparametric Magnetic Resonance Imaging Combined with Prostate-Specific Antigen Density in the Diagnosis of Prostate Cancer: An External Validation Study (2019)
- European Urology Oncology
(A1 Refereed original research article in a scientific journal) - Predicting the monetization percentage with survival analysis in free-to-play games (2019) 2019 IEEE Conference on Games (CoG 2019) Riikka Numminen, Markus Viljanen, Tapio Pahikkala
(A4 Refereed article in a conference publication )



