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Sobre

Sobre

Assistende de Investigação


Possui experiência em aprendizado de máquina, mineração de dados e extração de conhecimento de dados com especialidade em áreas como detecção de anomalias, séries temporais e padrões de mobilidade, dados espácio-temporais. 


Estudante do programa doutoral (Ph.D.) em Engenharia Informática na Universidade do Porto - MAPi

Bacharel em Sistemas de Informação e MBA em Gestão de Projetos. 


Possui certificações Project Management Professional (PMP), ITIL V3, COBIT, PSM e Scrum Fundamentals. 


Mais de 10 anos de experiência no desenvolvimento e manutenção de aplicações para Internet, Desktop e dispositivos móveis. Experiência adicional com modelagem UML e BPMN bem como framework Scrum. 


Gestor de Projetos com mais de 10 anos de experiência a coordenar equipas de desenvolvimento de software por meio da utilização de metodologias ágeis e tradicionais. 


Analista de Sistemas Sênior com mais de 8 anos de experiência na coleta e estruturação de requisitos de várias fontes, incluindo entrevistas com utilizadores, gestores executivos e na documentação e análise de sistemas legados. 


Especializações: Data Mining e Machine Learning, Projeto e Construção de Sistemas, Implantação e Integração de Soluções de Tecnololgia, Consultoria e Gestão de Projetos.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Thiago Andrade Silva
  • Cluster

    Informática
  • Cargo

    Assistente de Investigação
  • Desde

    26 outubro 2016
001
Publicações

2023

Study on Correlation Between Vehicle Emissions and Air Quality in Porto

Autores
Shaji, N; Andrade, T; Ribeiro, RP; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
Road transportation emissions have increased in the last few decades and have been the primary source of pollutants in urban areas with ever-growing populations. In this context, it is important to have effective measures to monitor road emissions in regions. Creating an emission inventory over a region that can map the road emission based on the vehicle trips can be helpful for this. In this work, we show that it is possible to use raw GPS data to measure levels of pollution in a region. By transforming the data using feature engineering and calculating the vehicle-specific power (VSP), we show the areas with higher emissions levels made by a fleet of taxis in Porto, Portugal. The Uber H3 grid system is used to decompose the city into hexagonal grids to sample nearby data points into a region. We validate our experiments on real-world sensor datasets deployed in several city regions, showing the correlation with VSP and true values for several pollutants attesting to the method's usefulness.

2022

How are you Riding? Transportation Mode Identification from Raw GPS Data

Autores
Andrade, T; Gama, J;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
Analyzing the way individuals move is fundamental to understand the dynamics of humanity. Transportation mode plays a significant role in human behavior as it changes how individuals travel, how far, and how often they can move. The identification of transportation modes can be used in many applications and it is a key component of the internet of things (IoT) and the Smart Cities concept as it helps to organize traffic control and transport management. In this paper, we propose the use of ensemble methods to infer the transportation modes using raw GPS data. From latitude, longitude, and timestamp we perform feature engineering in order to obtain more discriminative fields for the classification. We test our features in several machine learning algorithms and among those with the best results we perform feature selection using the Boruta method in order to boost our accuracy results and decrease the amount of data, processing time, and noise in the model. We assess the validity of our approach on a real-world dataset with several different transportation modes and the results show the efficacy of our approach.

2020

Identifying Points of Interest and Similar Individuals from Raw GPS Data

Autores
Andrade, T; Gama, J;

Publicação
Mobility Internet of Things 2018 - EAI/Springer Innovations in Communication and Computing

Abstract

2020

Mining Human Mobility Data to Discover Locations and Habits

Autores
Andrade, T; Cancela, B; Gama, J;

Publicação
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT II

Abstract
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a density-based clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

2020

Gradient Boosting Machine and LSTM Network for Online Harassment Detection and Categorization in Social Media

Autores
Pereira, FSF; Andrade, T; de Carvalho, ACPLF;

Publicação
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT II

Abstract
We present a solution submitted to the Social Media and Harassment Competition held in collaboration with ECML PKDD 2019 Conference. The dataset used is as set of tweets and the first task was on the detection of harassment tweets. To deal with this problem, we proposed a solution based on a gradient tree-boosting algorithm. The second task was categorization harassment tweets according to the type of harassment, a multiclass classification problem. For this problem we proposed a LSTM network model. The solutions proposed for these tasks presented good predictive accuracy.