I am a researcher and developer in computer science and software engineering at the Coordenação de Pesquisa Aplicada e Desenvolvimento Tecnológico (COPDT) of the Instituto Nacional de Pesquisas Espaciais (INPE) in Brazil. I am also a professor of the Programa de Pós-Graduação em Computação Aplicada (CAP) at INPE. My main research interests, education, and other relevant information are described below. In 2019, I carried out a post-doc at the Computational Optimisation and Learning (COL) Lab, School of Computer Science, University of Nottingham, England (United Kingdom).

RESEARCH

Machine and Deep Learning

Machine learning (ML) and particularly its most famous subfield nowadays, deep learning (DL), have revolutionised academia and industry. I am interested in developing and using ML/DL methods/techniques for a wide range of applications/problems including remote sensing, aerospace systems (drones, satellites), weather forecasting, image super-resolution, among others. Moreover, I have been exploring the use of ML ML and DL to help the software testing (see below Software Testing) activity for systems which produce non-trivial outputs such as scientific software applications.

Optimisation via Hyper-heuristics and Metaheuristics

Optimisation is a quantitative and systematic approach to search for the best/most appropriate solution among several possibilities. It is a field which has been of great interest for a long time by industry and academia being used in the context of disciplines such as computer science, engineering, and economics. I have been developing new selection hyper-heuristics and working with metaheuristics and hyper-heuristics within search-based software testing (SBST) (see below Software Testing), where the testing of a software system is formulated under an optimisation perspective. However, I am also interested in approaches to solve multi and many-objective optimisation problems related to space, automotive, natural resource management, and vehicle routing systems.

Software Testing

I have been working on the development of space software and systems for more than 20 years. During this time, I have been researching the field of software testing. My efforts have been in the tasks of generation and execution of test cases, and in the oracle problem where such studies are supported by the development of computational tools (SOLIMVA, GTSC, and others). I have been investigating test case generation via model-based testing (MBT) by means of formal methods, such as model checking (a formal verification method) and statecharts. Other directions I am following are the generation of test cases via combinatorial interaction testing (CIT), the automated oracle problem, and, as I have pointed out earlier, I have been working within SBST and using ML/DL to support testing.

EDUCATION

AWARDS

• Best Paper Award - 5th International Joint Conference on Advances in Computational Intelligence (IJCACI), 2021


• Best Paper Award - 21st International Conference on Computational Science and Its Applications (ICCSA), 2021


• Best Paper Award - 21st International Conference on Computational Science and Its Applications (ICCSA), 2021


• Best Paper Award - V Simpósio Brasileiro de Teste de Software Sistemático e Automatizado (SAST), 2020


• Best Paper Award - 19th International Conference on Computational Science and Its Applications (ICCSA), 2019


• Second Best Paper - II Simpósio Brasileiro de Teste de Software Sistemático e Automatizado (SAST), 2017


• Best Paper Award - I Simpósio Brasileiro de Teste de Software Sistemático e Automatizado (SAST), 2016


• Third Best Paper - XVIII Brazilian Symposium on Formal Methods (SBMF), 2015


• Third Best Paper/Tool - Sessão de Ferramentas, III Congresso Brasileiro de Software: Teoria e Prática (CBSoft), 2012


• Best Paper/Tool Award - XIV Sessão de Ferramentas, XXI Simpósio Brasileiro de Engenharia de Software (SBES), 2007


• Second Best Paper/Tool Award - XIII Sessão de Ferramentas, XX Simpósio Brasileiro de Engenharia de Software (SBES), 2006


• Third Best Project - Software Productivity and Quality Award, Brazilian Program on Software Productivity and Quality (PBQP Software), 2006

Last Update: August/2023