Tapio Pahikkala
Associate Professor
aatapa@utu.fi +358 29 450 4323 +358 50 345 5824 Agora Työhuone: 456D ORCID-tunniste: https://orcid.org/0000-0003-4183-2455 |
Machine learning, Data science, Artificial intelligence
Tapio Pahikkala currently holds an associate professorship of machine learning with 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.
The course I am currently responsible of: ``Applications of Data Analysis'', consists of a series of practical cases studies that are each presented by different assistant teachers that act as clients of data scientists. The clients then introduce the problem the the data scientist should solve for them and the details of the data. The students' job is then implement the data analysis pipeline, train a predictive model, do a proper experimental design and carry out carry out statistical estimation of the prediction performance for each client. To achieve this, they study the accompanying course material that is currently in the form of both video lectures and reading material. All the clients' cases correspond to real cases from which our team has written research articles in the past. For example, the case concerning metal ion concentration prediction from drinking water is based on our research cooperation with the chemistry deparment of the University of Turku (Pihlasalo et al. 2016), the case on water permeability prediction in forestry for route planning of forest harvesters and the use of newly developed spatial cross-validation for estimating the prediction performance in that context is based on our cooperation with the Natural Resources Center of Finland (Pohjankukka et al. 2017), and the case concerning drug-target interaction prediction is based on our research cooperation with Institute for Molecular Medicine Finland (Pahikkala et al. 2015), to highlight a few. We have also had plans to involve cases from private companies in the future, such that would correspond to real commercial cases.
- Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games (2022)
- IEEE Transactions on Games
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Generalized vec trick for fast learning of pairwise kernel models (2022)
- Machine Learning
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Adaptive risk prediction system with incremental and transfer learning (2021)
- Computers in Biology and Medicine
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks (2021)
- IEEE Access
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer (2021)
- Journal of Magnetic Resonance Imaging
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Effect of oat β-glucan of different molecular weights on fecal bile acids, urine metabolites and pressure in the digestive tract – A human cross over trial (2021)
- Food Chemistry
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Modeling drug combination effects via latent tensor reconstruction (2021) Wang Tianduanyi, Szedmak Sandor,Wang Haishan, Aittokallio Tero, Pahikkala Tapio, Cichonska Anna,Rousu Juho
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - A general-purpose toolbox for efficient Kronecker-based learning (2020) JuliaCon Proceedings Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets
(Muu (O2)) - Algebraic shortcuts for leave-one-out cross-validation in supervised network inference (2020)
- Briefings in Bioinformatics
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets (2020)
- International Journal of Medical Informatics
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography (2020)
- PLoS ONE
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - GeFeS: A generalized wrapper feature selection approach for optimizing classification performance (2020)
- Computers in Biology and Medicine
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects (2020)
- Nature Communications
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Measuring Player Retention and Monetization Using the Mean Cumulative Function (2020)
- IEEE Transactions on Games
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Negative Predictive Value of Biparametric Prostate Magnetic Resonance Imaging in Excluding Significant Prostate Cancer: A Pooled Data Analysis Based on Clinical Data from Four Prospective, Registered Studies (2020)
- European Urology Focus
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Optimized reference spectrum for rating the facade sound insulation (2020)
- Journal of the Acoustical Society of America
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - Predicting profitability of peer-to-peer loans with recovery models for censored data (2020)
- International Conference on Intelligent Decision Technologies
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - Predicting Unemployment with Machine Learning Based on Registry Data (2020)
- Lecture Notes in Business Information Processing
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - Prediction of prostate cancer aggressiveness using 18 F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI (2020)
- Scientific Reports
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - 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
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))