Professor Samuel Kaski, the new director of a center of excellence, wants to augment science by using machine learning
Samuel Kaski says that machine learning research can contribute to the efficiency of science by linking new research results to previous findings by modelling their data sets.
- Molecular biology is an example of an empirical data-driven scientific field, in which research results are currently related to earlier research mainly by means of papers written about them. It would be great if the measurement data could speak more for themselves. That could happen with the help of modelling. We showed that relevant studies can be found better by modelling data sets, than with keyword searches. By modelling we even noticed that the identifiers of a few papers were wrong in the database, and for those studies data retrieved by other means would have been incorrect, Kaski describes.
At the moment the work of Professor Kaski includes leading both the Centre of Excellence and the research institute, in addition to his own research work.
- It seems that there are different types of phases in the work of a professor. Until summer I will have more leadership duties, but research work must never stop, Kaski says.
Professor Samuel Kaski was appointed as the Director of COIN in the beginning of February. The change of director was planned already three years ago when the COIN was established. In addition to the COIN, Kaski is the Director of the Helsinki Institute of Technology HIIT, where his five-year term will end in August. Kaski is on leave from his own professorship at the Department of Computer Science.
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