Gustavo Lacerda

Responsável por liderar a equipe de tecnologia da Enacom, mantendo o alto nível da atividade core: a tecnologia. Com o crescimento acelerado, enfrenta o desafio de garantir a excelência científica das soluções entregues aos clientes e o ótimo desempenho dos softwares. Apresenta sólida expertise em tecnologias da Indústria 4.0, como otimização, gêmeo digital e cloud. Tem vasta experiência na implantação de sistemas de grande porte em setores como energia, siderurgia, logística, telecomunicações e governo. Possui doutorado em Engenharia Elétrica pela UFMG, com foco em Big Data.

Professor of Software Engineering, System Quality, Algorithms, Networks, DataBase I, Machine Learning, Distributed Systems, Parallel Programming and Cloud Computing, Artificial Inteligence.

Professor of Distributed Systems and Mobile Disciplines

Project Manager

Java Team Leader.

Java Software Architect

Systems Analyst

Java Developer

Title: Clustering Methods for BigDatasets. My PhD dissertation presents a methodology focused on clustering problems with large data volumes. The goal is to design algorithms that can process large volumes of data without loss of clustering quality. Specifically, My Doctoral dissertation presents two novels, fast and scalable distance-based clustering algorithms well suited to analyze large datasets. The first one is the GPIC clustering method, which performs the calculation of the affinity matrix and the eigenvectors with the support of the Graphics Processing Unit - GPU. The second the method, called bdrFCM, reduces the volume of data using the border of the Fuzzy c-means cluster results as a fundamental principle. Results found with synthetic and real datasets demonstrate that the approaches proposed by this work can process a significant amount of data in less time and reduce the volume of data, whilst maintaining the quality of the clustering result.

Title: Mutiagent System Applied in Parking a Lot problem