Centre of Excellence in Computational Inference, COIN

Organization profile

Profile Information

The Finnish Center of Excellence in Computational Inference Research (COIN) develops methods for transforming the data produced by the current data revolution into useful information. The key methodology for achieving this goal is statistical and computational inference based on the data.

The emphasis is on large data collections and computationally demanding modelling and inference algorithms. The mission is to push the boundary towards both more complex problems, requiring more sructured data models, and towards extremely rapid inference. COIN brings in expertise on several different approaches to inference, with a unique opportunity to address the core computational challenges with combinations of machine learning, computational statistics, statistical physics, and constraint-based search and optimization.

COIN works on two flagship applications. In the Intelligent Information Access flagship, the challenge is to make use of massive interrelated information sources, whether in everyday life or in science, and select what information to present to the user. The inference needs to be done on-line, learning relevance from the user's responses.

In the Computational Biology and Medicine flagship, COIN develops methods for maximally utilizing the novel measurement databases and structured stochastic models in making data-driven biology cumulative. In addition to these two flagship applications, COIN works on a few additional test-bench applications in collaboration with selected top-level application partners, from science and industry.

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Profiles

Photo of Mudassar Abbas

Mudassar Abbas

Person: Doctoral students

20112018
Photo of Muhammad Ammad-Ud-Din
20142017

Research Output

Allen’s Interval Algebra Makes the Difference

Janhunen, T. & Sioutis, M., 1 Jan 2020, Declarative Programming and Knowledge Management - Conference on Declarative Programming, DECLARE 2019, Unifying INAP, WLP, and WFLP, Revised Selected Papers. Hofstedt, P., Abreu, S., John, U., Kuchen, H. & Seipel, D. (eds.). SPRINGER , p. 89-98 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12057 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  • Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

    Järvenpää, M., Vehtari, A. & Marttinen, P., 2020, (Accepted/In press) Conference on Uncertainty in Artificial Intelligence (UAI 2020).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Boosting Answer Set Optimization with Weighted Comparator Networks

    Bomanson, J. & Janhunen, T., 2020, In : Theory and Practice of Logic Programming. 20, 4, p. 512-551

    Research output: Contribution to journalArticleScientificpeer-review

  • Prizes

    2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up

    Luana Micallef (Recipient), 2016

    Prize: Award or honor granted for a specific work

    AAAI Conference Awards: Outstanding Senior Program Committee Member

    Jussi Rintanen (Recipient), 2017

    Prize: Award or honor granted for academic career

    Aalto SCI Award 2018 for Innovation of the year

    Samuel Kaski (Recipient), 2018

    Prize: Award or honor granted for a specific work

    Activities

    Data analysis with humans

    Samuel Kaski (Speaker)
    2020

    Activity: Talk or presentation typesPublic or invited talk

    1st International Workshop on AI for Smart TV Content Production, Access and Delivery

    Raphael Troncy (Member), Jorma Laaksonen (Member), Hamed Rezazadegan Tavakoli (Member), Lyndon Nixon (Member), Vasileios Mezaris (Member)
    2019

    Activity: Participating in or organising an event typesOrganization of a workshop, panel, session or tutorial

    Acting as opponent to doctoral student

    Jorma Laaksonen (Examiner)
    2019

    Activity: Examination typesPre-examination of dissertation or acting as opponent to doctoral students