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Rui Carlos Mendes de Oliveira
I am Associate Professor with Habilitation at the Informatics Department of University of Minho, where I teach Fault-Tolerant Distributed Systems. I serve as a member of the Board of Directors of INESC TEC, director of the Minho Advanced Computing Centre, and co-director of the UT Austin Portugal program. I am the national representative to the EuroHPC JU.
I received my PhD. degree in 2000 from the École Polytechnique Fédérale de Lausanne. My main research contributions have been in the field of fault-tolerant distributed agreement and epidemic communication algorithms and in the conception, development and assessment of dependable database systems. I have coordinated the H2020 SafeCloud project on secure processing in the Cloud and the FP6 GORDA project on open database replication, two previous national projects on scalable dependable databases, ESCADA and StrongRep, and the U. Minho team in the FP7 CumuloNimbo and LeanBigdata projects. I published over 100 research papers on large scale and dependable distributed systems, and served on the programme committee of several highly reputed conferences. I chaired the PC of IFIP DAIS and IEEE SRDS, and served as general chair of SRDS and ACM Eurosys.
Domains
Publications
Privacy-Preserving Machine Learning on Apache Spark
Brito, CV;Ferreira, PG;Portela, BL;Oliveira, RC;Paulo, JT;
2023
IEEE ACCESS
Toward a Practical and Timely Diagnosis of Application's I/O Behavior
Esteves, T;Macedo, R;Oliveira, R;Paulo, J;
2023
IEEE ACCESS
LOOM: A Closed-Box Disaggregated Database System
Coelho, F;Alonso, AN;Ferreira, L;Pereira, J;Oliveira, R;
2023
PROCEEDINGS OF12TH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE AND SECURE COMPUTING, LADC 2023
Diagnosing applications' I/O behavior through system call observability
Esteves, T;Macedo, R;Oliveira, R;Paulo, J;
2023
53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2023 - Workshops, Porto, Portugal, June 27-30, 2023
Supervised theses
Explainable Artificial Intelligence – Detecting biases for Interpretable and Fair Face Recognition Deep Learning Models
Ana Dias Teixeira de Viseu Cardoso
UP-FEUP
