Data and Systems Intelligence

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Information

Description:

The research line on Data and Systems Intelligence (DSI) strives to devise innovative solutions for large‐scale data problems and complex systems. DSI intersects areas of recent high visibility and impact, such as Machine Learning and Data Mining, but also the general fields of Artificial Intelligence. This highly multidisciplinary research line, aims at advancing research and knowledge in the recent flagship application and research domain of Data Science, Web Science, and Network Science; promotes synergies with other research lines regarding data centric research; and bridges training, research, and industry by supervising MSc students at the new Master in Data Science at FCUL, and highly motivated PhD students.

Targeted areas:

Data Science
Web Science
Network Science
Data Mining
Machine Learning
Artificial Intelligence
Logic in Computer Science

Leader:

Sara C. Madeira

Members

Research Line Members:

Name	Category
Alcides Fonseca	Integrated Researcher
Alexey Ignatiev	Integrated Researcher
Alysson Bessani	Integrated Researcher
André Souto	Integrated Researcher
Andreia Mordido	Integrated Researcher
António Morgado	Integrated Researcher
Cátia Pesquita	Integrated Researcher
Fernando Ramos	Integrated Researcher
Francisco Couto	Integrated Researcher
Helena Aidos	Integrated Researcher
Ibéria Medeiros	Integrated Researcher
José Cecílio	Integrated Researcher
Naercio Magaia	Integrated Researcher
Pedro Ferreira	Integrated Researcher
Sara C. Madeira	Integrated Researcher
Sara Silva	Integrated Researcher
Tiago Guerreiro	Integrated Researcher
João E. Batista	PhD Student
Márcia Barros	PhD Student
Pedro Barbosa	PhD Student
Telma Pereira	PhD Student
Fábio Martins	MSc Student
Joana Matos	MSc Student
Jorge Cardoso	MSc Student
Madalena Pavão	MSc Student
Nuno Rodrigues	MSc Student
Pedro Ruas	MSc Student
Rita Sousa	MSc Student
Sofia Jesus	MSc Student
Teemu Tervo	MSc Student
Sofia Pires	Collaborator
Sofia Teixeira	Collaborator

Software and Tools

Software and Tools:

Title	Short descritption
AgreementMakerLight	An automated and efficient ontology matching system derived from AgreementMaker that has been in development since the beginning of 2013
CESSM	An online tool for the automated evaluation of GO-based semantic similarity measures

Publications

2017

Journal Article

Yadav, P., Mateus P., Paunković N., & Souto A. (2017). Quantum contract signing with entangled pairs. arXiv preprint arXiv:1711.09843.

Costa, P. A. R. S., Ramos F. M. V., & Correia M. (2017). On the Design of Resilient Multicloud MapReduce. IEEE Cloud Computing. 4, 74–82.

Antunes, L., Bauwens B., Souto A., & Teixeira A. (2017). Sophistication vs Logical Depth. Theory of Computing Systems. 60, 280–298.

Lamurias, A., Clarke L. A., & Couto F. M. (2017). Extracting microRNA-gene relations from biomedical literature using distant supervision. PloS one. 12, e0171929.

Mateus, P., Sernadas A., & Souto A. (2017). Universality of quantum Turing machines with deterministic control. Journal of Logic and Computation. 27, 1–19.

Alcântara, J., Oliveira T., & Bessani A. (2017). GINJA: One-dollar Cloud-based Disaster Recovery for Databases.

Pereira, T., Lemos L., Cardoso S., Silva D., Rodrigues A., Santana I., et al. (2017). Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows. BMC medical informatics and decision making. 17, 110.

Keller, A., Gerkin R. C., Guan Y., Dhurandhar A., Turu G., Szalai B., et al. (2017). Predicting human olfactory perception from chemical features of odor molecules. Science. eaal2014.

Conference Paper

Cruz-Filipe, L., Marques-Silva J., & Schneider-Kamp P. (2017). Efficient certified resolution proof checking. International Conference on Tools and Algorithms for the Construction and Analysis of Systems. 118–135.

Peñaloza, R., Mencía C., Ignatiev A., & Marques-Silva J. (2017). Lean Kernels in Description Logics. European Semantic Web Conference. 518–533.

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