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.
Current research projects:Academy of Finland: "AI technologies for interaction prediction in biomedicine", Academy of Finland: "Machine Learning for Systems Pharmacology", Business Finland: "Privasa".
The course I am currently responsible of: ``Evaluation of Machine Learning Methods'', 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.
- Co-Regularized Least-Squares for Label Ranking (2010) Preference Learning Tsivtsivadze E, Pahikkala T, Boberg J, Salakoski T, Heskes H
(A3 Refereed book chapter or chapter in a compilation book) - Feature Selection for Regularized Least-Squares: New Computational Short-Cuts and Fast Algorithmic Implementations (2010) Proceedings of the Twentieth IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010) Pahikkala T, Airola A, Salakoski T
(A4 Refereed article in a conference publication ) - Fuzzy Logic Based Control for Parallel Cascade Control (2010)
- International Journal on Automatic Control and System Engineering
(A1 Refereed original research article in a scientific journal) - Greedy {RankRLS}: a Linear Time Algorithm for Learning Sparse Ranking Models (2010) SIGIR 2010 Workshop on Feature Generation and Selection for Information Retrieval Pahikkala T, Airola A, Naula P, Salakoski T
(A4 Refereed article in a conference publication ) - Large scale training methods for linear {RankRLS} (2010) Proceedings of the {ECML/PKDD} 2010 Workshop on Preference Learning {(PL-10)} Airola A, Pahikkala T, Salakoski T
(A4 Refereed article in a conference publication ) - Learning Intransitive Reciprocal Relations with Kernel Methods (2010)
- European Journal of Operational Research
(A1 Refereed original research article in a scientific journal) - Proceedings of the 14th Finnish Artificial Intelligence Conference, STeP 2010 (2010) Pahikkala T, Väyrynen J, Kortela J, Airola A (eds. )
(C2 Editorial work for a scientific compilation book) - Speeding up Greedy Forward Selection for Regularized Least-Squares (2010) Proceedings of The Ninth International Conference on Machine Learning and Applications (ICMLA 2010) Pahikkala T, Airola A, Salakoski T
(A4 Refereed article in a conference publication ) - An efficient algorithm for learning to rank from preference graphs (2009)
- Machine Learning
(A1 Refereed original research article in a scientific journal) - Efficient Hold-Out for Subset of Regressors (2009)
- Lecture Notes in Computer Science
(A4 Refereed article in a conference publication ) - Extracting Complex Biological Events with Rich Graph-Based Feature Sets (2009) Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task Björne J, Heimonen J, Ginter F, Airola A, Pahikkala T, Salakoski T
(A4 Refereed article in a conference publication ) - Locality kernels for sequential data and their applications to parse ranking (2009)
- Applied Intelligence
(A1 Refereed original research article in a scientific journal) - Matrix representations, linear transformations, and kernels for disambiguation in natural language (2009)
- Machine Learning
(A1 Refereed original research article in a scientific journal) - A Graph Kernel for Protein-Protein Interaction Extraction (2008) Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP 2008) Airola A, Pyysalo S, Björne J, Pahikkala T, Ginter F, Salakoski T
(A4 Refereed article in a conference publication ) - All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning (2008)
- BMC Bioinformatics
(A1 Refereed original research article in a scientific journal) - A Sparse Regularized Least-Squares Preference Learning Algorithm (2008)
- Frontiers in Artificial Intelligence and Applications
(A4 Refereed article in a conference publication ) - Efficient AUC Maximization with Regularized Least-Squares (2008)
- Frontiers in Artificial Intelligence and Applications
(A4 Refereed article in a conference publication ) - Exact and Efficient Leave-Pair-Out Cross-Validation for Ranking RLS (2008) Proceedings of the 2nd International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2008) Pahikkala T, Airola A, Boberg J, Salakoski T
(A4 Refereed article in a conference publication ) - Machine Learning to Automate the Assignment of Diagnosis Codes to Free-text Radiology Reports: a Method Description (2008) Proceedings of the ICML/UAI workshop on Machine Learning in health care applications Suominen H, Ginter F, Pyysalo S, Airola A, Pahikkala T, Salanterä S, Salakoski T
(A4 Refereed article in a conference publication ) - Regularized Least-Squares for Learning Non-Transitive Preferences between Strategies (2008)
- Publications of the Finnish Artificial Intelligence Society
(A4 Refereed article in a conference publication )
