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About

About

Senior Researcher / Professor -



Senior Researcher with experience in machine learning, data mining, and knowledge extraction from data with expertise in areas such as anomaly detection, time series and mobility patterns, and spatiotemporal data.


Assistant Professor in the Management and Science and Technology departments of the Universidade Portucalense D. Henrique and Assistant Professor in the IT department of the Sciences and Engineering faculties of the University of Porto.


He has a PhD in Computer Engineering (Data Mining & Machine Learning) from the Universities of Minho, Aveiro, and Porto – MAP-i program.

He has a degree in Computer Engineering (Information Systems) and an MBA in Project Management.


He holds Project Management Professional (PMP), ITIL V3, COBIT, PSM, and Scrum Fundamentals certifications.


IT professional with over 10 years of experience developing and maintaining applications for the Internet, Desktop, and mobile devices. Additional experience with UML and BPMN modeling and Scrum framework.


Project Manager with more than 10 years of experience coordinating software development teams using agile and traditional methodologies.


Senior Systems Analyst with more than 8 years of experience in collecting and structuring requirements from various sources, including interviews with users, and executive managers, and in documenting and analyzing legacy systems.


Specializations: Data Mining & Machine Learning, Systems Design and Construction, Implementation and Integration of Technology Solutions, Consulting and Project Management.

Interest
Topics
Details

Details

  • Name

    Thiago Andrade Silva
  • Role

    Senior Researcher
  • Since

    26th October 2016
  • Nationality

    Brasil
  • Contacts

    +351222094398
    thiago.a.silva@inesctec.pt
001
Publications

2024

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

Authors
Andrade, T; Gama, J;

Publication
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC 2024, Avila, Spain, April 8-12, 2024

Abstract

2023

Study on Correlation Between Vehicle Emissions and Air Quality in Porto

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

Publication
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

Authors
Andrade, T; Gama, J;

Publication
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

Authors
Andrade, T; Gama, J;

Publication
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

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

Publication
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.