I am an Associate Professor (Maître de Conférences - tenured/permanent position) at the University of Lille, and a member of the MAGNET team at INRIA Lille - NORD Europe and the CRISTAL lab (France).
My research mainly focuses on developing scalable Machine Learning algorithms for networked data and assessing their accuracy performance both in theory and in practice.
I received an MSc degree (Summa cum Laude) in Computer Science from the University of Insubria and a PhD in Machine Learning / Computer Science from the Milan University, working under the supervision of Prof. Nicolò Cesa-Bianchi and Prof. Claudio Gentile.
Previously, during my BSc and MSc, I spent about one year at the Computer Science Department of the University of Copenhagen, working with Prof. Jyrki Katajainen on designing novel and efficient data structures and assessing their practical performance.
I have also some industrial experience: I worked at SAS Institute (Statistical Analysis System) in Italy and Bloomberg LP in Switzerland.
Recent awards
      Neural Information Processing Systems - NeurIPS (NIPS) 2021.
- Stephen Pasteris, Mark Herbster, Fabio Vitale, Massimiliano Pontil.
A Gang of Adversarial Bandits.
Proceedings of the 35th conference on Neural Information Processing Systems - NeurIPS (NIPS) 2021. - Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M. Procopiuc, Claudio Gentile.
Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees.
Proc. of the 38th International Conference on Machine Learning - ICML 2021 - Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster.
Online Learning of Facility Locations.
Proceedings of the 32nd International Conference on Algorithmic Learning Theory - ALT 2021. - Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale.
Correlation Clustering with Adaptive Similarity Queries.
Proceedings of the 33rd conference on Neural Information Processing Systems - NeurIPS (NIPS) 2019. - Fabio Vitale, Anand Rajagopalan, Claudio Gentile.
Flattening a Hierarchical Clustering through Active Learning.
Proceedings of the 33rd conference on Neural Information Processing Systems - NeurIPS (NIPS) 2019. - Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster.
MaxHedge: Maximising a Maximum Online.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics - AISTATS 2019. - F. Vitale, N. Parotsidis, C. Gentile.
Online reciprocal recommendation with theoretical performance guarantees
Proceedings of the 32nd conference on Neural Information Processing Systems - NeurIPS (NIPS) 2018. (Full paper)
- S. Pasteris, F. Vitale, C. Gentile, M. Herbster.
On Similarity Prediction and Pairwise Clustering
Proceedings of the 29th International Conference on Algorithmic Learning Theory - ALT 2018. - G. Le Falher, N. Cesa-Bianchi, C. Gentile, F. Vitale.
On the troll-trust model for edge sign prediction in social networks
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics - AISTATS 2017. - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella.
Efficient Link Classification in Social Networks
International Conference on Computational Social Science - ICCSS 2015. - M.Herbster, S. Pasteris, F. Vitale.
Online Sum-Product Computation over Trees
Proceedings of the 26th Conference on Neural Information Processing Systems - NeurIPS (NIPS) 2012. - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
A linear time active learning algorithm for link classification
Proceedings of the 26th Conference on Neural Information Processing Systems - NeurIPS (NIPS) 2012.
(Extended version of "A Linear Time Active Learning Algorithm for Link Classification", 29th International Conference on Machine Learning - ICML 2012 - Workshop: Mining and Learning with Graphs). - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
A correlation clustering approach to link classification in signed networks
25th Annual Conference on Learning Theory. - COLT 2012 - JMLR Workshop and Conference Proceedings, 23:34.1-34.20, 2012. - F. Vitale, N. Cesa-Bianchi, C. Gentile, and G. Zappella
See the tree through the lines: the Shazoo algorithm
Proceedings of the 25th Annual Conference on Neural Information Processing Systems - NeurIPS (NIPS) 2011. (Full paper)
- M. Herbster, S. Pasteris and F.Vitale
Efficient Prediction for Tree Markov Random Fields in a Streaming Model
25th Annual Conference on Neural Information Processing Systems - NeurIPS (NIPS) 2011 - Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback. - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
Active Learning on Graphs via Spanning Trees
24th Annual Conference on Neural Information Processing System - NeurIPS (NIPS) 2010 - Workshop: Networks Across Disciplines in Theory and Applications. - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
Active Learning on Trees and Graphs
Proceedings of the 23rd Annual Conference on Learning Theory - COLT 2010. - N. Cesa-Bianchi, C. Gentile, F. Vitale, and G. Zappella
Random Spanning Trees and the Prediction of Weighted Graphs
Proceedings of the 27th International Conference on Machine Learning - ICML 2010. - N. Cesa-Bianchi, C. Gentile and F. Vitale
Fast and Optimal Algorithms for Weighted Graph Prediction
23rd Annual Conference on Neural Information Processing System - NeurIPS (NIPS) 2009 - Workshop: Analyzing Networks and Learning with Graphs. - N. Cesa-Bianchi, C. Gentile and F. Vitale
Learning Unknown Graphs
Proceedings of the 20th International Conference on Algorithmic Learning Theory - ALT 2009. - N. Cesa-Bianchi, C. Gentile and F. Vitale
Fast and Optimal Prediction on a Labeled Tree
Proceedings of the 22nd Annual Conference on Learning Theory - COLT 2009. - N. Cesa-Bianchi, C. Gentile and F. Vitale
Online Graph Prediction with Random Trees
22nd Annual Conference on Neural Information Processing System - NeurIPS (NIPS) 2008 - Workshop: New Challenges in Theoretical Machine Learning: Data Dependent Concept Spaces. - C. Gentile, F. Vitale and C. Brotto
On Higher-Order Perceptron Algorithms
Proceedings of the 21st Annual Conference on Neural Information Processing System - NeurIPS (NIPS) 2007.
Journal publications
- N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella
Random Spanning Trees and the Prediction of Weighted Graphs
Journal of Machine Learning Research, 14:1251-1284, 2013. - N. Cesa-Bianchi, C. Gentile, F. Vitale
Predicting the labels of an unknown graph via adaptive exploration
Theoretical Computer Science, 412(19):1791-1804, 2011, Special Issue on Algorithmic Learning Theory 2009. - J. Katajainen, F. Vitale
Navigation Piles with Applications to Sorting, Priority Queues, and Priority Deques.
Nordic Journal of Computing 10 (3), 238-262, 2003.
(Included by Donald E. Knuth in "The Art of Computer Programming" -- Section 7.1.3 - Bit tricks & techniques -- Vol. 4A). [Google Books - link])
Peer-reviewed conference publications
- Bloomberg LP, Switzerland (2012/2013).
- SAS Institute (Statistical Analysis System), Italy (2007).
-
BrainTree (London, UK); ISI Foundation (Turin, Italy), 2019, 2020. Title: Compressing graph information for binary node classification.
- BrainTree (London, UK); INRIA Lille Nord Europe (Lille, France); Workshop FouLarD 2018
- Project funded by ANR
(Agence Nationale de la Recherche).
Project title: (PAMELA) - Personalized and decentrAlized MachinE Learning under constrAints (https://project.inria.fr/pamela/).
Duration: 2016-2020.
Partners (https://project.inria.fr/pamela/members/#asap): Inria Lille - Team Magnet, IRISA Rennes 1 University - Team ASAP, IRISA Rennes 1 University - Team Cidre, LIP6 - University of Paris 6 - Team MLIA.
Industrial partners: Snips, Mediego.
- Inria North-European Labs
(International partnership).
Project title: (RSS) - Rankings and Similarities in Signed graphs (https://raweb.inria.fr/rapportsactivite/RA2015/magnet/uid59.html).
Duration: late 2015 to late 2017.
Partners: Prof. Aristides Gionis (Data Mining Group, Aalto University, Finland) and Prof. Mark Herbster (Centre for Computational Statistics and Machine Learning, University College London, UK).
- SCAGLIA
(Scalable Graph Algorithms for Learning in Networked Data
JCJC "Jeunes Chercheuses et Jeunes Chercheurs" INS2I 2015
PI: Fabio Vitale).CRISTAL
Centre de Recherche en Informatique, Signal et Automatique de Lille. - MAGNET team
(Machine Learning in information NETworks). - PASCAL 2 Network of Excellence
(Pattern Analysis, Statistical Modelling and Computational Learning)
EU, Seventh Programme Framework. - Data-dependent geometries and structures
("pump-priming project" within Pascal2). - Google-sponsored project SPAN
(Scalable Prediction Algorithms for Networked Data - Google Research Award, January 2010). - PASCAL Network of Excellence
(Pattern Analysis, Statistical Modelling and Computational Learning)
EU, Sixth Programme Framework. - Generic programming - Algorithms and Tools
Danish Natural Science Research Council, 2005. (Project description). - Performance Engineering Laboratory
University of Copenhagen.
Visits to universities and research institutes
- May 2019: Google Research, New York (USA - Collaboration with Prof. Claudio Gentile).
- September 2018: University College London, Department of Computer Science (United Kingdom - Collaboration with Dr. Stephen Pasteris).
- April 2018: INRIA Lille Nord Europe (France - Member of the PhD committee of Geraud Le Falher).
- November 2017: University of Milan, Department of Computer Science (Italy - collaboration with Prof. Nicolò Cesa-Bianchi).
- March 2016; April 2016, May 2016: University of Milan, Department of Computer Science (Italy - collaboration with Prof. Nicolò Cesa-Bianchi and Prof. Claudio Gentile).
- July 2015; August 2015: Helsinki Aalto University, Department of Computer Science (Finland - collaboration with Prof. Aristides Gionis).
- May 2014: University of Milan, Department of Computer Science (Italy - collaboration with Prof. Nicolò Cesa-
Bianchi and Prof. Claudio Gentile).
- November 2013: University of Milan, Department of Computer Science (Italy - collaboration with Prof. Nicolò Cesa-Bianchi and Prof. Claudio Gentile);
University College London, Department of Computer Science (United Kingdom - collaboration with Prof. Mark Herbster);
University of Copenhagen, Department of Computer Science (Denmark - collaboration with Prof. Jyrki Katajainen)); - July 2013: Lille University & INRIA Lille - Nord Europe (France).
- August 2012: Microsoft Research, (Redmond, USA).
- November 2010; September 2011; November 2011; February 2012, May 2012: University College London, Department of Computer Science (United Kingdom - collaboration with Prof. Mark Herbster).
- November 2008: University of Bonn, Department of Computer Science (Germany).
- September 2002: University of Copenhagen, Department of Computer Science (Denmark).
Teaching and tutoring (until 2016)
In French universities the teaching load is set at 192 hours per year spent in the classroom with students (or the equivalent thereof). - Introduction to Algorithms
University of Lille (Undergraduate level course - Academic Year 2015-2016, Fall semester). - Graph Algorithms
University of Lille (Undergraduate level course - Academic Year 2015-2016, Spring semester). - Information Coding
University of Lille (Undergraduate level course - Academic Year 2014-2015, Spring semester). - Unsupervised Classification
University of Lille (Graduate level course - Academic Year 2014-2015, Spring semester). - Unsupervised Classification
University of Lille (Undergraduate level course - Academic Year 2014-2015, Spring semester). - Graph Algorithms
University of Lille (Undergraduate level course - Academic Year 2014-2015, Fall semester). - Unsupervised Classification
University of Lille (Graduate level course - Academic Year 2013-2014, Spring semester). - Artificial Intelligence
University of Lille (Undergraduate level course - Academic Year 2013-2014, Spring semester). - Information Technology and Internet Certificate ("Certificat informatique et Internet")
University of Lille (Undergraduate level course - Academic Year 2013-2014, Spring semester). - Information and Communication Technology for Education ("Technologies de l'Information et de la Communication pour l'Enseignement")
University of Lille (Undergraduate level course - Academic Year 2013- 2014, Spring semester). - Statistical Methods for Machine Learning
University of Milan (Graduate level course - Academic Year 2009-2010 Spring semester) - Java Programming Laboratory
University of Milan (Undergraduate level course - Academic Year 2009-2010 Fall semester) - Java Programming Laboratory
University of Milan (Undergraduate level course - Academic Year 2008-2009 Fall semester)
International schools
- Bertinoro international Spring School, (Bertinoro, Italy), 2009.
- Bristol Summer School on Probabilistic Techniques in Computer Science, University of Bristol (United Kingdom), 2008.
- International PhD School on Randomized Algorithms (BiCi-SNS), Scuola Normale di Pisa (Italy), 2008.