Paulo Mann

PhD Student
Institute of Computing, UFF

I am PhD student working with Aline Paes and Elton Hiroshi Matsushima. My current research is to develop Machine Learning pipelines to detect depressed individuals, to shed light upon those who do not know his/her condition and guide them to adequate treatment. From a practical point of view, we use textual and visual cues obtained solely from users created content on social media.

In addition to my research, I am very interested in chatbots and how they can be used to help people with Major Depressive Disorder (MDD), similar to how Woebot, which implements a Cognitive Behavioral Therapy.


Selected Research

  • Towards Safer (Smart) Cities: Discovering Urban Crime Patterns Using Logic-based Relational Machine Learning

    Vítor Lourenço, Paulo Mann, Artur Guimarães, Aline Paes, Daniel de Oliveira
    In Submission. 2018
    [Abstract] [Paper]

    Smart cities initiatives have the potential to improve the life of citizens in a huge number of dimensions. One of them is the development of techniques and services capable of contributing to the enhancement of security public policies. Finding criminal patterns from historical data would arguably help in predicting and even preventing thefts and burglaries that continuously increase in urban centers worldwide. However, accessing such history and finding patterns across the interrelated crime occurrences data are challenging tasks, particularly to underdevelopment countries. In this paper, we address these problems by combining three techniques: we collect crime data from existing crowd-sourcing systems, we automatically induce patterns with relational machine learning, and we manage the entire process using scientific workflows. The framework developed under these lines is named CRiMINaL (Crime patteRn MachINe Learning). Experimental results conducted from a popular Brazilian source of data and a traditional relational learning system shows that CRiMINaL is a promising tool to induce interpretable models that can assist police departments on crime prevention.