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Sobre

Sobre

Investigador Sénior / Professor -



Investigador Sénior com 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 espaciotemporais.


Professor Auxiliar nos departamentos de Gestão e Ciência e Tecnologia da Universidade Portucalense D. Henrique e Professor Assistente do departamento de Informática das faculdades de Ciências e Engenharia da Universidade do Porto.


Possui doutoramento em Engenharia Informática (Data Mining & Machine Learning) pelas Universidades do Minho, Aveiro e Porto – programa MAP-i.

É Licenciado em Engenharia Informática (Sistemas de Informação) e MBA em Gestão de Projetos.


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


Profissional de T.I com 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 e 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 & Machine Learning, Projeto e Construção de Sistemas, Implantação e Integração de Soluções de Tecnologia, Consultoria e Gestão de Projetos.

 


Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Thiago Andrade Silva
  • Cargo

    Investigador Sénior
  • Desde

    26 outubro 2016
001
Publicações

2024

Where DoWe Go From Here? Location Prediction from Time-Evolving Markov Models

Autores
Andrade, T; Gama, J;

Publicação
39TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2024

Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This work presents an approach for location prediction using a probabilistic model and data mining techniques over mobility data streams. We evaluate the method over 5 real-world datasets. The results show the usefulness of the proposal in comparison with other-well-known approaches.

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.

2023

Estimating Instantaneous Vehicle Emissions

Autores
Andrade, T; Gama, J;

Publicação
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023

Abstract

2023

Which Way to Go - Finding Frequent Trajectories Through Clustering

Autores
Andrade, T; Gama, J;

Publicação
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings

Abstract
Trajectory clustering is one of the most important issues in mobility patterns data mining. It is applied in several cases such as hot-spots detection, urban transportation control, animal migration movements, and tourist visiting routes among others. In this paper, we describe how to identify the most frequent trajectories from raw GPS data. By making use of the Ramer-Douglas-Peucker (RDP) mechanism we simplify the trajectories in order to obtain fewer points to check without losing information. We construct a similarity matrix by using the Fréchet distance metric and then employ density-based clustering to find the most similar trajectories. We perform experiments over three real-world datasets collected in the city of Porto, Portugal, and in Beijing China, and check the results of the most frequent trajectories for the top-k origins x destinations for the moves. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis

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

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Abstract
Over the past few decades, road transportation emissions have increased. Vehicles are among the most significant sources of pollutants in urban areas. As such, several studies and public policies emerged to address the issue. Estimating greenhouse emissions and air quality over space and time is crucial for human health and mitigating climate change. In this study, we demonstrate that it is feasible to utilize raw GPS data to measure regional pollution levels. By applying feature engineering techniques and using a microscopic emissions model to calculate vehicle-specific power (VSP) and various specific pollutants, we identify areas with higher emission levels attributable to a fleet of taxis in Porto, Portugal. Additionally, we conduct network analysis to uncover correlations between emission levels and the structural characteristics of the transportation network. These findings can potentially identify emission clusters based on the network's connectivity and contribute to developing an emission inventory for an urban city like Porto.