Dr. Tiago Tamagusko | Computer Vision | Best Researcher Award

Postdoctoral Research Fellow at University College Dublin, Ireland

Dr. Tiago Tamagusko is a Transportation Specialist and Data Scientist with a strong academic and professional background in intelligent transportation systems, computer vision, and applied AI. He currently serves as a Postdoctoral Research Fellow at University College Dublin, contributing to the REALLOCATE Mobility project. His work combines advanced data science, geospatial technologies, and machine learning to address urban mobility challenges. He has participated in award-winning hackathons and contributed to both academic research and innovative startups.

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Education Background

• Ph.D. in Transport Systems, University of Coimbra, Portugal (2020–2024)
  Thesis: Artificial Intelligence applied to Transport Infrastructure Management

• M.Sc. in Urban Mobility Management, University of Coimbra, Portugal (2018–2020)
  Dissertation: Airport Pavement Design

• B.Sc. in Civil Engineering, Federal University of Santa Catarina, Brazil (2008–2013)
  Final Project: Cost of Lack of Standardization of Railway Gauges in Brazil

• Technical Degree in Computer Networks & Telecommunications, Federal Institute of Santa Catarina, Brazil (2002–2004)

Professional Development

• Postdoctoral Research Fellow, University College Dublin, Ireland (2024–Present)
  REALLOCATE Mobility Project – AI and urban mobility

• Researcher, CITTA – Research Centre for Territory, Transports and Environment, Portugal (2020–2024)
  Focus: AI in transport systems

• Data Scientist, JEST – Junior Enterprise for Science and Technology, Portugal (2020–2022)
  Led Technology & Innovation Team

• Civil Engineer/Researcher, LabTrans/UFSC, Brazil (2013–2018)
  Research on ITS, road infrastructure and HS-WIM systems

• Intern, LabTrans/UFSC, Brazil (2009–2013)
  Developed software for Brazil’s national transport infrastructure

• Telecom Technician, Alcatel (Alcatel-Lucent Enterprise), Brazil (2004–2005)
  Developed access control systems using PHP

Research Focus

Dr. Tamagusko’s research explores the intersection of artificial intelligence and transportation. His focus areas include machine learning, computer vision, geospatial data science, road infrastructure, and intelligent transportation systems (ITS). He is especially passionate about leveraging AI to enable smarter, safer, and more sustainable urban mobility.

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Publication Top Notes

1. Building Back Better: The COVID-19 Pandemic and Transport Policy Implications for a Developing Megacity

Authors: Hasselwander, M.; Tamagusko, T.; Bigotte, J.F.; Ferreira, A.; Mejia, A.; Ferranti, E.
Journal: Sustainable Cities and Society
Volume: 69
Article Number: 102864
Year: 2021
Pages: 1–13
DOI: 10.1016/j.scs.2021.102864
Citations: 116
Summary: This study explores how the COVID-19 pandemic has impacted transport policy in developing megacities, providing recommendations for sustainable urban mobility post-crisis.

2. Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population During the COVID-19 Pandemic

Authors: Tamagusko, T.; Ferreira, A.
Journal: Sustainability
Volume: 12
Issue: 22
Article Number: 9775
Year: 2020
Pages: 1–16
DOI: 10.3390/su12229775
Citations: 45
Summary: This paper uses mobile location data and geospatial analysis to evaluate how the pandemic affected population mobility trends in Portugal.

3. Deep Learning Applied to Road Accident Detection with Transfer Learning and Synthetic Images

Authors: Tamagusko, T.; Gomes Correia, M.; Huynh, M.A.; Ferreira, A.
Journal: Transportation Research Procedia
Volume: 64
Year: 2022
Pages: 90–97
DOI: 10.1016/j.trpro.2022.09.012
Citations: 30
Summary: This work presents a deep learning framework for road accident detection using transfer learning and synthetic image augmentation for improved accuracy and robustness.

4. Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

Authors: Tamagusko, T.; Ferreira, A.
Journal: Infrastructures
Volume: 8
Issue: 12
Article Number: 170
Year: 2023
Pages: 1–19
DOI: 10.3390/infrastructures8120170
Citations: 24
Summary: A comprehensive review of machine learning models used to predict the International Roughness Index (IRI), identifying challenges and proposing future research avenues in pavement performance forecasting.

5. Data-Driven Approach for Urban Micromobility Enhancement Through Safety Mapping and Intelligent Route Planning

Authors: Tamagusko, T.; Gomes Correia, M.; Rita, L.; Bostan, T.C.; Peliteiro, M.; Martins, R.; Santos, L.; Ferreira, A.
Journal: Smart Cities
Volume: 6
Issue: 4
Pages: 2035–2056
Year: 2023
DOI: 10.3390/smartcities6040094
Citations: 13
Summary: This paper introduces a data-driven system integrating street-level imagery and safety metrics to optimize micromobility route planning in urban environments.

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