Ph.D. in Computer Science
Network Science & Artificial Intelligence
Introduction. I'm Gabriel Spadon de Souza, known by my peers as Gabriel Spadon. Education. I have a Ph.D. in Computer Science with honors from the University of Sao Paulo (USP) - Brazil, part of which was carried out at the Georgia Institute of Technology (GaTech) - USA. Additionally, I have a Master's and bachelor's in Computer Science and an Associate's Degree in Information Technology. Research Interests. I like Computer Science as a whole, but I have worked closely with Network Science and Artificial Intelligence. Breakthroughs. In the field of Network Science, I proposed a way to mix complex networks and machine learning to forecast human mobility between municipalities through censuses-collected urban indicators. Though in Artificial Intelligence, I created a new graph-based layer architecture and neural network model for time series forecasting. Research Goals. My current research goal is to find ways to bring deep learning to network science to enhance our understanding of the non-linearities behind complex networks and dynamic systems.
Professional & Higher Education
[2017-2021] Ph.D. in Computer Science with honors @ University of São Paulo - USP
Thesis: From Cities to Series - Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics
Supervisor: José F. Rodrigues Jr., Ph.D.
[2015-2017] M.Sc. in Computer Science @ University of São Paulo - USP
Dissertation: Characterization of patterns and behaviors through the analytic processing of complex networks in urban systems
Supervisor: José F. Rodrigues Jr., Ph.D.
[2011-2015] B.Sc. in Computer Science @ São Paulo State University - UNESP
Monograph: Analysis and simulation of Apache Hadoop in virtual environments
Supervisor: Ronaldo C. M. Correia, Ph.D.
[2009-2010] Associate in Information Technology @ National Service of Commercial Learning - SENAC
Spadon G., Hong S., Brandoli B., Matwin S., Rodrigues-Jr J. F., and Sun J. — Pay attention to evolution: Time series forecasting with deep graph-evolution learning, 2021. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE; and,
Spadon G., Carvalho A. C. P. L. F., Rodrigues-Jr J. F., and Alves L. G. A. — Reconstructing commuters network using machine learning and urban indicators, 2019. Scientific Reports; and,
Spadon G., Machado B. B., Eler D. M., and Rodrigues-Jr J. F. — Detecting multi-scale distance-based inconsistencies in cities through complex-networks (invited paper), 2019. Journal of Computational Science, Elsevier.
Spadon G., Gimenes G., and Rodrigues-Jr J. F. — Topological street-network characterization through feature-vector and cluster analysis, 2018. 18th International Conference on Computational Science, Springer;
Spadon G., Machado B. B., Eler D. M., and Rodrigues-Jr J. F. — A distance-based tool-set to track inconsistent urban structures through complex-networks (awarded paper), 2018. 18th International Conference on Computational Science, Springer; and,
Spadon G., Scabora L. C., Araujo M. V., Oliveira P. H., Machado B. B., Sousa E. P. M., Traina-Jr C., and Rodrigues-Jr J. F. — Complex-network tools to understand the behavior of criminality in urban areas, 2018. 15th International Conference on Information Technology: New Generations, Springer.
Spadon G. and Rodrigues-Jr J. F. — Computer-assisted city-touring for explorers, 2018. Workshop on Big Social Data and Urban Computing @ 44th International Conference on Very Large Data Bases, CEUR-WS;
Spadon G., Gimenes G., and Rodrigues-Jr J. F. — Identifying urban inconsistencies via street networks, 2017. 17th International Conference on Computational Science @ Procedia Computer Science, Elsevier; and,
Spadon G., Scabora L. C., Oliveira P. H., Araujo M. V., Machado B. B., Sousa E. P. M., Traina-Jr C., and Rodrigues-Jr J. F. — Behavioral characterization of criminality spread in cities, 2017. 17th International Conference on Computational Science @ Procedia Computer Science, Elsevier.
Grants & Awards
[05/2022] Grant & Fellowship
Title: Ocean Monitoring for Environmental Protection using Heterogeneous Spatio-Temporal Data using Neural and Complex Networks
Institutions: Ocean Frontier Institute (Canada), Dalhousie University (Canada), and ISBlue & IMT Atlantique (France)
[06/2021] Grant & Fellowship
Title: Vessel mobility patterns analysis and forecasting on irregular timing data using noise-robust neural networks
Institutions: Marine Environmental Research Infrastructure for Data Integration and Application Network (MERIDIAN)
[03/2019] Grant & Fellowship
Title: Advancing medical prognosis based on graph concepts and artificial neural networks
Identifier: FAPESP #2019/04461-9
[02/2019] Grant & Fellowship
Title: Analysis and improvement of urban systems using digital maps in the form of complex networks
Identifier: FAPESP #2017/08376-0
Title: Best articles of the 18th International Conference on Computational Science, ICCS 2018 - Wuxi, China
[10/2017] Grant & Fellowship
Title: Pattern modeling and knowledge discovery in urban systems using complex networks and machine learning
Identifier: CNPq #167967/2017-7
[12/2015] Grant & Fellowship
Title: Characterization of patterns and behaviors through the analytical processing of real-world complex networks
Identifier: CAPES #9254601/M
[01/2015] Grant & Scholarship
Title: Emergency support for multi-user equipment at UNESP (laboratory specialist)
Identifier: PROPe #008/2015
Title: Best articles of the Workshop on Scientific and Technological Initiation and Independent Projects (VII WICT-PI), ICMC - USP
[01/2014] Grant & Scholarship
Title: Support for multi-user and thematic projects (laboratory specialist)
Identifier: PROPe #008/2014