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A. Sirbu, Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V. D. P., Stumme, G., Theunis, J., Tria, F., and Van den Bossche, J., Participatory Patterns in an International Air Quality Monitoring Initiative., PLoS One, vol. 10, p. e0136763, 2015.
A. Sirbu and Babaoglu, O., Towards operator-less data centers through data-driven, predictive, proactive autonomics, Cluster Computing, pp. 1–14, 2016.
A. Sirbu, Kerr, G., Crane, M., and Ruskin, H. J., RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering., PLoS One, vol. 7, p. e50986, 2012.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., Opinion dynamics: models, extensions and external effects, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 363–401.
A. Sirbu, Ruskin, H. J., and Crane, M., Integrating heterogeneous gene expression data for gene regulatory network modelling., Theory Biosci, vol. 131, pp. 95-102, 2012.
A. Sirbu, Ruskin, H. J., and Crane, M., Cross-platform microarray data normalisation for regulatory network inference., PLoS One, vol. 5, p. e13822, 2010.
A. Sirbu, Ruskin, H. J., and Crane, M., Comparison of evolutionary algorithms in gene regulatory network model inference., BMC Bioinformatics, vol. 11, p. 59, 2010.
A. Sirbu and Babaoglu, O., Predicting System-level Power for a Hybrid Supercomputer, in 2016 International Conference on High Performance Computing Simulation (HPCS), Innsbruck, Austria, 2016.
A. Sirbu and Babaoglu, O., A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q, in Euro-Par 2015: parallel Processing Workshops, LNCS 9523, 2015.
A. Sirbu and Babaoglu, O., Towards Data-Driven Autonomics in Data Centers, in IEEE International Conference on Cloud and Autonomic Computing, 2015.
A. Sirbu, Crane, M., and Ruskin, H. J., Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks, Microarrays, vol. 4, pp. 255–269, 2015.
M. Setzu and Atzori, M., SPARQL Queries over Source Code, in 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016.
V. D. P. Servedio, Caminiti, S., Gravino, P., Loreto, V., Sirbu, A., and Tria, F., Large Scale Engagement Through Web-Gaming and Social Computations, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 237–254.
R
S. Ruggieri, Introduction to the special issue on Artificial Intelligence for Society and Economy, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
S. Ruggieri, Hajian, S., Kamiran, F., and Zhang, X., Anti-discrimination analysis using privacy attack strategies, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014.
S. Ruggieri, Using t-closeness anonymity to control for non-discrimination., Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
S. Ruggieri, Eirinakis, P., Subramani, K., and Wojciechowski, P., On the complexity of quantified linear systems, Theoretical Computer Science, vol. 518, pp. 128–134, 2014.
S. Ruggieri, Data Anonymity Meets Non-discrimination, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
S. Ruggieri, Learning from polyhedral sets, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri and Turini, F., A KDD process for discrimination discovery, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
S. Ruggieri, Enumerating Distinct Decision Trees, in International Conference on Machine Learning, 2017.
S. Ruggieri and Mesnard, F., Typing Linear Constraints for Moding CLP() Programs, in SAS, 2008, pp. 128-143.
A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., Effective injury forecasting in soccer with GPS training data and machine learning, PloS one, vol. 13, p. e0201264, 2018.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., NDlib: a python library to model and analyze diffusion processes over complex networks, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
G. Rossetti, Guidotti, R., Pennacchioli, D., Pedreschi, D., and Giannotti, F., Interaction Prediction in Dynamic Networks exploiting Community Discovery, in International conference on Advances in Social Network Analysis and Mining, ASONAM 2015, Paris, France, 2015.

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