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.