Dr. André Lage-Freitas
LaCCAN - Laboratório de Computação Científica e Análise Numérica - Universidade Federal de Alagoas (UFAL) - Brazil
Dr. André Lage-Freitas is an Assistant Professor at Universidade Federal de Alagoas (Brazil) and
member of LaCCAN lab (Laboratório de Computação Científica e Análise Numérica).
He holds a Ph.D. in Computer Science from the Institut National des Sciences Appliquées de Rennes
(INRIA, France). His main research interest lies on how to ease the use of Cloud computing infrastructures
for Data Scientists by focusing on big remote sensing data sets.
He held for two years the Research Coordinator (Manager) position under
the UFAL Vice-Presidency of Research and Graduate Course. He has experience
in international research projects such as CloudArray project (coordinator)
funded by Microsoft Azure Research and further international projects funded by
European Union and Latin America, e.g., EOxposure H2020, S-CUBE FP7, CONICYT (Chile),
PUC Valparaiso, and FAPEAL (Brazil). He is a member of the IEEE and of the SBC (Sociedade Brasileira de Computação).
He is Counsellor of IEEE-UFAL Student Branch and Enactus UFAL. He reviews research projects for FAPESP
(São Paulo Research Foundation) and he is a reviewer of the IEEE Geoscience and Remote Sensing Letters journal.
Google Scholar: http://goo.gl/mlPor0
Lattes CV: http://lattes.cnpq.br/3203407648310274
Effortless huge imagery processing in the Cloud - The case of the Image Processing Service (IPS)
Cloud computing technologies provide on-demand services for computing and storing a huge amount of data.
Nevertheless, using these services implies handling low-level operational details such as creating, starting,
stopping virtual instances in addition to dealing with data storage issues. In this talk, I'll present some current
challenges and the Image Processing Service (IPS) which enables the processing of huge data sets by using cloud computing
through an easy user interface. We validated the preliminary version of IPS by processing gigabytes of fully
Polarimetric Synthetic Aperture Radar (PolSAR) data sets in the Microsoft Azure Cloud. The evaluation uses actual
PolSAR data collected from the NASA Unmanned Aerial Vehicle SAR (UAVSAR) Project.
The methodology employed is fully reproducible and relies on Free/Libre and Open Source Software (FLOSS).