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    About

    About

    I am a professor at the Scientific Area of Mathematics in the School of Technology and Management of the Polytechnic Institute of Viana do Castelo (ESTG-IPVC) and member of the Laboratory in Artificial Intelligence and Decision Support (LIAAD – INESC TEC) of the University of Porto. I have a Msc in Mathematics by the University of Minho and a PhD in Applied Mathematics by the University of Porto in 2014.

    My main research lines are Data Analysis; Symbolic Data Analysis (Analysis of multidimensional complex data) and Linear regression models. I work in the development and application of methods adapted to data carrying a lot of information. This research is included in the framework of Symbolic Data Analysis.

    Moreover, I collaborate with bio-informaticians and chemistry researchers for the development of mathematical models applied to agent-based modelling. 

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    Details

    Details

    • Name

      Sónia Dias

    • Role

      Senior Researcher

    • Since

      01st April 2012
    001
    Publications
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    2022

    Analysis of Distributional Data

    Authors
    Brito, P; Dias, S;

    Publication

    Abstract

    2022

    Regression Analysis with the Distribution and Symmetric Distribution Model

    Authors
    Dias, S; Brito, P;

    Publication
    Analysis of Distributional Data

    Abstract

    2022

    Descriptive Statistics based on Frequency Distribution

    Authors
    Dias, S; Brito, P;

    Publication
    Analysis of Distributional Data

    Abstract

    2022

    Fundamental Concepts about Distributional Data

    Authors
    Dias, S; Brito, P;

    Publication
    Analysis of Distributional Data

    Abstract

    2021

    Discriminant analysis of distributional data via fractional programming

    Authors
    Dias, S; Brito, P; Amaral, P;

    Publication
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

    Abstract
    We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights.

    CloseRead Abstract
    Supervised
    thesis

    2017

    Building Origin-Destination matrices from big data sources

    Author
    Manish Bhanu

    Institution
    Outra

    View all Supervised Theses