HIIT Wide Focus Area: Augmented Science


If you are not redirected automatically, follow the link to example

The advances in science depend on effectively building upon the results that others have achieved, which are based on previously collected data. This brings to the forefront a challenge for computational science to better utilize the massive explosion in digital scientific data, whether it is scientific literature, or raw measurement data from previous studies. HIIT has begun focusing resources on a HIIT-wide focus area, titled Augmented Science, which builds on the existing excellences and touches most of HIIT research topics. Augmented science develops information technology methods and pilot applications for making the data-driven fields such as modern biology cumulative, and revolutionizing the way we search and access scientific resources and literature. Improving the general problem solving method of science, in collaboration with the other fields, is the best way for HIIT to contribute to solving the grand challenges of the humanity.

We aim to support scientific information access by enabling better coordination of communicating ideas and scientific results within the scientific community. Results of scientific efforts are traditionally published as articles and communicated personally as presentations or other related materials. The rapid communication of knowledge between researchers is a key success factor to enable better science. The volume of scientific output is estimated to be millions of publications worldwide per year; the growth rate of PubMed alone is more than 1 article per minute. The problem of communication that the scientific community is facing is shifting from publishing and sharing the information to finding and filtering the suitable materials to support every day work of researchers. We aim to help scientists to better find and manage the content that they use in their everyday work.

Key publications

D. GÅ‚owacka, T. Ruotsalo, K. Konyushkova, K. Athukorala, S. Kaski, and G. Jacucci. Directing exploratory search: Reinforcement learning from user interactions with keywords. In Proceedings of IUI’13, International Conference on Intelligent User Interfaces, to appear.
S. Seth, N. Välimäki, S. Kaski, and A. Honkela. Exploration and retrieval of whole-metagenome sequencing samples. arXiv:1308.6074 [q-bio.GN]

Key participant groups and researchers

Samuel Kaski (HIIT Director, Professor at Aalto University)
Giulio Jacucci (Professor at University of Helsinki)
Petri Myllymäki (Professor at University of Helsinki)
Veli Mäkinen (Professor at University of Helsinki)
Antti Honkela (Project co-coordinator, Academy Research Fellow at University of Helsinki)
Tuukka Ruotsalo (Project co-coordinator, Postdoctoral Researcher at Aalto University)


Last updated on 18 Dec 2014 by Webmaster - Page created on 21 May 2012 by Tuukka Ruotsalo