News

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.

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.

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

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. http://www.hiit.fi/mlseminar

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.

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.

The study utilises model checking to address the insufficiencies of testing and simulation in the verification of safety systems.

The object of Jussi Lahtinen’s dissertation was to find a more formal and mathematical approach to system verification and to develop model checking practices that are suitable for the nuclear industry. The traditional system verification methods, such as testing and simulation, do not have enough coverage to address the increasing digitalisation of safety automation systems.

Research data was collected from Kenyan national parks over the past 60 years, combined with traits of the teeth of herbivorous mammals.

In the new study, the annual rainfall and average temperatures in the national parks are inferred from the teeth of herbivorous mammals. Such reverse engineering opens up new opportunities for interpreting fossil records. The results were recently published in the journal PNAS.

HIIT scientists Michael Gutmann and Corander published a machine learning based ABC inference approach in the Journal of Machine Learning Research. Their method (BOLFI) is based on Bayesian optimization with Gaussian processes and is generally applicable to simulator models with intractable likelihoods. Without sacrificing accuracy, BOLFI speeds up posterior computation by 3-4 orders of magnitude compared with the state-of-the-art sequential Monte Carlo algorithms.