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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. 



  • Name

    Thiago Andrade Silva
  • Role

    External Student
  • Since

    26th October 2016


Anomaly Detection in Sequential Data: Principles and Case Studies

Andrade, T; Gama, J; Ribeiro, RP; Sousa, W; Carvalho, A;

Wiley Encyclopedia of Electrical and Electronics Engineering



Discovering Common Pathways Across Users’ Habits in Mobility Data

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques. © 2019, Springer Nature Switzerland AG.