Tiago Tamagusko

Transportation Specialist & Data Scientist

Enthusiast of AI, programming, and active mobility.

Experience

2020-2024 Researcher, CITTA, Portugal.
(4yr. PhD Researcher)

• Development and application of AI and ML for road infrastructure management;
• Utilization of computer vision techniques for pavement condition assessment;
• Implementing deep learning for accident detection.

2020-2022 Project manager, JEST, Portugal.
(6mos. Intern, 4mos. Data Scientist, 1yr. Innovation&Technology Coordinator, 8mos. Project manager)

• Application of computer vision (OpenCV, Deep Learning, and Yolov5) for real-time monitoring;
• Develop an NLP algorithm to apply the General Data Protection Regulation (GDPR).

2009-2018 Civil Engineer | Researcher, LabTrans, Brazil.
(4yr. Intern and 5yr. Engineer)

• Technical studies and development of projects for weigh-in-motion systems;
• Engineering economics, cost-benefit and life-cycle analysis of pavements;
• Implementation of ITS technologies for real-time highway monitoring;
• Design and Construction of continuously reinforced concrete pavement;
• Design and construction of asphalt pavement;
• GIS-Based Network Analysis for the national road network (Brazil).

2004-2005 Telecommunications Technician, Alcatel-Lucent Enterprise, Brazil.
(Telecommunications Technician)

• Assignments in the infrastructure field.

Projects

2023+ Lisbonhub, CycleAI
(Data Analyst/Scientific Advisor)

• Solution using computer vision and deep learning to identify risk points for cyclists and micromobility in Greater Lisbon.
  The Project won the VoxPop Lisbon call and was co-financed by the EU.

Website: cycleai.net/data

More: voxpoplisboa.pt

2022 World Road Quality Visualization, Portfolio
(Data Analyst/Dev)

• Visualization of road infrastructure quality in the world.

Website: roadquality.tamagusko.com

More: github.com/tamagusko/road-quality

2022 Intelligent Real-time Accident Warning System (IRAWS), Nordic AI & Open Data Hackathon.
(Backend dev)

• Transfer learning with deep learning to train a model to detect road accidents.

More: github.com/tamagusko/nordicopendata

2019 Studies for the Viseu Airport Runway, Coimbra University, Portugal.
(Student)

• Data analysis study for the design of the Viseu Airport runway.

More: github.com/tamagusko/ViseuAirportStudy

2013-2015 Development of new technologies for HS-WIM, LabTrans, Brazil.
(Researcher)

• Development of a new High-Speed-Weigh-In-Motion (HS-WIM) model.

More: PIAF Project (LabTrans)

Education

2020 Ph.D., Transport Systems, University of Coimbra, Portugal.
(In progress)
Project of Thesis: “Artificial Intelligence applied to Transport Infrastructure Management”

2018-2020 M.Sc., Urban Mobility Management, University of Coimbra, Portugal.
Master dissertation: “Airport Pavement Design”

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

2003-2004 Tech., Telecommunications, Federal Institute of Santa Catarina, Brazil.

2002-2003 Tech., Computer Network, Federal Institute of Santa Catarina, Brazil.

Publications

2023 Tamagusko, T.; Ferreira, A. (2023). Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions. Infrastructures, 8(12), 170, 1-19. MDPI. DOI: 10.3390/infrastructures8120170.

2023 Tamagusko, T.; Gomes Correia, M.; Rita, L.; Bostan, TC; Peliteiro, M.; Martins, R.; Santos, L.; Ferreira, A. (2023). Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning. Smart Cities, 6(4), 2035-2056, 1-22. MDPI. DOI: 10.3390/smartcities6040094.

2023 Rita, L.; Peliteiro, M.; Bostan, TC.; Tamagusko, T.; and Ferreira, A. (2023). Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London. Sustainability, 15(13), 10270, 1-27. MDPI. DOI: 10.3390/su151310270

2023 Tamagusko, T., and Ferreira, A. (2023). Optimizing pothole detection in pavements: A comparative analysis of deep learning models, in Proceedings of the Second International Conference on Maintenance and Rehabilitation of Constructed Infrastructure Facilities. Honolulu, Hawaii. 16-19 August. DOI: 10.3390/engproc2023036011.

2023 Tamagusko, T., and Ferreira, A. (2023). Pavement performance prediction using machine learning: Supervised learning with tree-based algorithms, in Proceedings of the World Conference on Transport Research. Montreal, Canada, 17-21 July. SUBMITTED.

2022 Tamagusko, T., Correia, M., Huynh, M. and Ferreira, A. (2022). Deep Learning applied to Road Accident Detection with Transfer Learning and Synthetic Images, Transportation Research Procedia, 64, 90-97. DOI: 10.1016/j.trpro.2022.09.012

2022 Tamagusko, T. and Ferreira, A. (2022). Data analysis applied to the airport pavement design, in Proceedings of the 7th International Conference on Road and Rail Infrastructure, CD Ed., pp. 419-425, Pula, Croatia.

2021 Marc Hasselwander, Tiago Tamagusko, João Bigotte, Adelino Ferreira, Alvin Mejia, and Emma J. S. Ferranti (2021). Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity. Sustainable Cities and Society. DOI: 10.1016/j.scs.2021.102864

2020 Tamagusko, T. and Ferreira, A. (2020). Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic. Sustainability, 12, 9775. MDPI. DOI: 10.3390/su12229775

2020 Tamagusko, T. and Ferreira, A. (2020). Software Tools for Airport Pavement Design. In: Rocha Á., Adeli H., Reis L., Costanzo S., Orovic I., Moreira F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer. DOI: 10.1007/978-3-030-45691-7_7

2020 Tamagusko, T. (2020). Airport Pavement Design. DOI: 10.13140/RG.2.2.19628.00640

2016 Guerson, L. P.; Gevaerd, B. M.; Otto, G. G.; Tamagusko, T.; Valente, A. M. (2016). Test Site for Evaluation of High-Speed WIM and ITS Solutions in Brazilian Conditions. In: ICWIM7 Proceedings, 2016. Proceedings: icwim7_bookproceedings

Awards/Achievements

2023 Location Intelligence for Smart Cities Hackathon, 2nd | Online Hackathon.

2022 Transatlantic AI Hackathon – Sustainable Supply Chain DeepHack, 3rd | Online Hackathon.

2022 Nordic AI & Open Data Hackathon, Top Team in the online stage and finalist | Online Hackathon.

2020 FCT PhD Research Scholarship (2020-2024)

2020 Merit Board of the top 5% students at UC, in the academic year 2019-2020

2019 Merit Board of the top 5% students at UC, in the academic year 2018-2019

Courses

Academic

2023 Innovation and Science Diplomacy School on Global Circulation of Research Data, USP, Brazil, 60h.

Computer Science

2022 NCC Portugal AI for Science Bootcamp, UC/UMinho / NVIDIA | Online, 7h.

2021 Mastering Big Data with Open Source Platforms, USP | Online, 32h.

2020 Machine Learning, Coursera/Stanford | Online, 11 weeks.

Transport Infrastructure

2020 Think Road Safety, Open Learning Campus | World Bank Group | Online, 1.5h.

2016 Theory and Practice of WIM Systems, FAPEU, Brazil, 10h.

2014 HDM4 Software Operation, FAPEU, Brazil, 40h.

Skills

Complex problem solving;
Critical and analytical thinking;
Passion for learning;
Flexibility and adaptability;
Programming Language – Python, R, Matlab, SQL;
Data Analysis – Python, SQL, GIS, Excel;
Machine Learning – Python, Scikit-learn, Tensorflow, Keras, Streamlit, Pytorch.

Programming: Learning pathway

2022+ Python: Geospatial Analisys and Deep Learning (Pytorch).
2021+ Python: Backend (Flask, FastAPI) and NLP.
2020+ Python: Computer Vision and Deep Learning (Keras/Tensorflow).
2018+ Python: Data Science and Machine Learning.
2018-2020 R: Data Analysis.
2013-2018 Python + Excel + Matlab: Data Analysis.
2005-2006 Pascal + C: Academia.
2004 PHP + PostgreSQL: Web development.
2003 8051 Assembly: Microcontroller programming.

Tools

Autocad, ArcGIS, QGIS;
SQL, MySQL, SQLite, PostgreSQL;
Advanced in MS Excel;
HDM-4, FAARFIELD, PTV Visum;
ArcGIS, QGIS;
Python, R, Matlab;
Github, Heroku, AWS;
Jupyter, Colab, Linux, Docker; Agile, Scrum, Trello; Slack.

Last updated: February 2024