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About

About

Research Assistant / I.T Project Manager. 

Has experience in Machine Learning, Data-Mining, and Knowledge Discovery especially in areas such as Anomaly Detection, Time Series, and Mobility Patterns, Spatio-temporal data. 

Computer Science Ph.D. Student at the University of Porto - MAP-i. 
Holds a Computer Information Systems Bachelors degree and MBA in Project Management. 

Project Management Professional (PMP), Agile enthusiast, Professional Scrum Master (PSM) e Scrum Fundamentals Certified, ITIL V3, COBIT Certified. 

More than 10 years of experience developing and managing applications for the Internet, desktop, and mobile. Extended experience with UML and BPMN modeling, as well as with SCRUM process framework. 

Project Manager with more than 9 years of experience coordinating software development teams running agile and traditional methodologies. 

Senior Business Analyst with more than 8 years of experience collecting and structuring requirements from various sources, including end-user interviews, corporate stakeholders, documentation, and legacy system analysis. 

Specialties: Data Mining, Knowledge Discover, and Machine Learning, Design and Systems Development, Deployment and Integration Solutions IT Consulting and Project Management. 

Interest
Topics
Details

Details

  • Name

    Thiago Andrade Silva
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    26th October 2016
001
Publications

2022

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

Authors
Andrade, T; Gama, J;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract

2020

Identifying Points of Interest and Similar Individuals from Raw GPS Data

Authors
Andrade, T; Gama, J;

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

Abstract

2020

Mining Human Mobility Data to Discover Locations and Habits

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

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

Abstract

2020

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

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

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

Abstract

2020

Discovering locations and habits from human mobility data

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

Publication
ANNALS OF TELECOMMUNICATIONS

Abstract
Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. 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 (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals' visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual's habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.