• HIIT scientists make a breakthrough in genome-wide epistasis analysis

    Fri, 17.02.2017

    Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data, suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces.

  • Best student paper award at WSDM 2017

    Fri, 10.02.2017

    At this year's International Conference on Web Search and Data Mining (WSDM 2017), the best student paper award went to “Reducing Controversy by Connecting Opposing Views” by Venkata Rama Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis, from Aalto University and Qatar Computing Research Institute.

  • Election candidates engage in battles also in social media

    Thu, 02.02.2017

    In the recent work on "Working the fields of big data: Using big-data-augmented online ethnography to study candidate–candidate interaction at election time" published in Journal of Information Technology & Politics, Salla-Maaria Laaksonen, Matti Nelimarkka, Mari Tuokko, Mari Marttila, Arto Kekkonen and Mikko Vili explore how ethnography can be used to support computational data analysis, developing a novel observation that candidates engage in candidate-candidate interaction and even battles in social media.

  • Discovering the evolution of Burkholderia pseudomallei, a dangerous tropical soil bacterium

    Sun, 29.01.2017

    A HIIT research team led by professor Jukka Corander collaborated with the pathogen genomics group at Wellcome Trust Sanger Institute to unearth the evolution of Burkholderia pseudomallei, a notorius soil bacterium causing serious human infections in tropics. Contrary to previous understanding, the genomic analyses revealed that the origin of B. pseudomallei isolates on the American continent is in Africa, dating back to the peak period of slave trade.

  • A new mutation mechanism was found in human and bacterial genomes

    Thu, 05.01.2017

    An international research team has found a new replacement mechanism that causes mutations in both humans and bacteria. The mechanism can cause several changes to a short stretch of DNA simultaneously. The research was conducted by observing fragments of DNA sequence that contained plenty of mutations.

  • ELFI: Engine for Likelihood-Free Inference

    Wed, 04.01.2017

    HIIT researchers have developed an engine for likelihood-free inference (ELFI), a Python framework for simulator-based statistics, which is useful in Bayesian inference when the likelihood function is difficult to evaluate or unknown. Press release

  • Machine Learning Coffee Seminar

    Tue, 03.01.2017

    Starting January 9, Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee before and after the talk. The talks will start 9:15, with coffee served from 9:00.

  • EEG reveals information essential to users

    Thu, 08.12.2016

    For the first time, information retrieval is possible with the help of EEG interpreted with machine learning.

    In a study conducted by the Helsinki Institute for Information Technology (HIIT) and the Centre of Excellence in Computational Inference (COIN), laboratory test subjects read the introductions of Wikipedia articles of their own choice. During the reading session, the test subjects’ EEG was recorded, and the readings were then used to model which key words the subjects found interesting.


  • Preethi Lahoti feels very privileged to be part of the Data Mining research group

    Fri, 04.11.2016

    Honours programme student Preethi Lahoti conducts research in graph mining and social-networks analysis.

    Exceptionally qualified Master’s students have joined the honours programme in computer science. Altogether 15 Master’s students from all over the world will have hands-on experience in the actual computer science research. Majority of the honours programme students are specialized in the machine learning, data mining and probabilistic modelling research area.