Helsinki Institute for Information Technology (HIIT), Aalto University, Finland
Established in 2010, the Aalto University is a new university with centuries of experience. The Aalto University was created from the merger of three Finnish universities: The Helsinki School of Economics, Helsinki University of Technology and The University of Art and Design Helsinki. The most important focus areas of research at Aalto University are computational science and modeling, materials research, design, and ICT and media. More information on Aalto University can be found at http://www.aalto.fi
Helsinki Institute for Information Technology HIIT is a joint institute of the two top universities of Finland, Aalto University and University of Helsinki. The mission of HIIT is to do fundamental IT research and to bring it to other fields. HIIT's strong areas of expertise include bioinformatics, algorithmics and machine learning, in which HIIT groups belong to four national Centres of Excellence and PASCAL EU Network of Excellence. HIIT has recently started an institute-wide focus area on computational methods for data-intensive biological research. More information on HIIT can be found at http://www.hiit.fi
Our core expertise is on large-scale data-intensive computational modeling and inference, especially with multiple data sources.
The research group of Professor Samuel Kaski, director of HIIT, develops machine learning methods for integrating and analyzing multiple data sources, in particular for systems biology and computational medicine problems. Professor Samuel Kaski has published almost 200 peer-reviewed papers on statistical machine learning, computational data-analysis, computational biology and medicine, bioinformatics, proactive interfaces, and neural networks (Google h-index 34, g-index 72). Kaski is Director of HIIT, Vice Director of Finnish Centre of Excellence in Computational Inference Research COIN, and Director of Finnish Doctoral Programme in Computational Sciences FICS. He has strong expertise in leading research consortia and projects. More information on Professor Kaski's group can be found at http://research.ics.tkk.fi/mi/
In the strong areas of bioinformatics, systems biology, data analysis and data mining, key people from Aalto University include Professor Heikki Mannila and Dr Jaakko Hollmén in addition to prof. Kaski's group. Additionally, HIIT has relevant PI's in University of Helsinki in the same fields, including Professors Esko Ukkonen, Veli Mäkinen and Hannu Toivonen, and Dr Antti Honkela.
Selected publications relevant to ITFoM
1) J. Caldas, N. Gehlenborg, A. Faisal, A. Brazma, and S. Kaski. Probabilistic retrieval and visualization of biologically relevant microarray experiments. Bioinformatics, 25:i145–i153, 2009. (ISMB/ECCB 2009).
2) I. Huopaniemi, T. Suvitaival, J. Nikkilä, M. Orešič, and S. Kaski. Multivariate multi-way analysis of multi-source data. Bioinformatics, 26:i391–i398, 2010. (ISMB 2010).
3) A. Klami and S. Kaski. Local Dependent Components. Proc. 24th International Conference on Machine Learning (ICML-07), pages 425-432, ACM, 2007.
4) G. Leen, J. Peltonen, and S. Kaski. Focused multi-task learning using Gaussian processes. In D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis, ed., Machine Learning and Knowledge Discovery in Databases (Proc. ECML PKDD 2011), Part II , pages 310–325. Springer Berlin / Heidelberg, 2011. Best Paper Award in Machine Learning.
5) S. Rogers, A. Klami, J. Sinkkonen, M. Girolami, and S. Kaski. Infinite factorization of multiple non-parametric views. Machine Learning, 79:201–226, 2010.
6) M. Sysi-Aho, A. Ermolov, P. V. Gopalacharyulu, A. Tripathi, T. Seppänen-Laakso, J. Maukonen, I. Mattila, S. T. Ruohonen, L. Toukola, L. Yetukuri, T. Härkönen, E. Lindfors, J. Nikkilä, J. Ilonen, O. Simell, M. Saarela, M. Knip, S. Kaski, E. Savontaus, and M. Orešič. Metabolic regulation in progression to autoimmune diabetes. PLoS Computational Biology, accepted for publication.
7) J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research, 11:451–490, 2010.
8) S. Virtanen, A. Klami, and S. Kaski. Bayesian CCA via group sparsity. In L. Getoor and T. Scheffer, ed., Proc. 28th International Conference on Machine Learning (ICML-11), pages 457–464, ACM, New York, NY, 2011.