I am a Senior Researcher and Team lead at CENTAI Institute (CENTer for Artificial Intelligence, Turin, Italy). Previously, I was appointed as an Associate Professor (Maître de Conférences - tenured/permanent position) at the University of Lille (France), while I was a member of the Machine Learning in Information Networks team at INRIA Lille - Nord Europe.

Driven by a deep interest in advanced machine learning, I have honed my expertise in optimization, graph mining, graph neural networks, clustering and recommender systems. My research aims to craft efficient algorithms for gleaning knowledge from extensive networks, continually exploring novel applications to address complex issues in academia and industry.

As AI/Machine Learning Team Lead at CENTAI, I guide a group of dedicated researchers in advancing AI research and its practical applications, with a keen eye on Fintech innovations. We are motivated by the unwavering commitment to enrich theoretical knowledge in Artificial Intelligence and to create tangible solutions, fervently pursuing groundbreaking collaborations and advancements in this domain.


In addition to my current professional activity in industrial research at CENTAI, I have also garnered industrial experience working at SAS Institute (Statistical Analysis System) in Italy and Bloomberg LP in Switzerland.


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.




Recent professional activities

  • Lead guest editor of the machine learning journal track

          European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2023.



    Special recognition in my Bachelor

    Inclusion in Combinatorial Algorithms of Knuth's monograph

  • The data structure Navigation Piles presented in Navigation Piles with Applications to Sorting, Priority Queues, and Priority Deques (Nordic Journal of Computing 10 (3), 238-262, 2003) has been included in the multi-volume work The Art of Computer Programming ("Section 7.1.3 - Bit tricks & techniques -- Vol. 4A, [Google Books - link]) by Donald E. Knuth, recipient of the ACM Turing Award.



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    Publications




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    Industrial experience





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    Talks


    • Eurandom (Eindhoven, Netherland), 2024. Title: Fast and Effective GNN Training through Sequences of Random Path Graphs.

    • 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 (Bertinoro, Italy), 2018. Title: Online Reciprocal Recommendation with Theoretical Performance Guarantees.

    • Copenhagen University (Denmark), 2013. Title: Machine Learning on Trees and Graphs.

    • Lille University & INRIA Lille - Nord Europe (France), 2013. Title: Fast Prediction Algorithms for Networked Data.

    • Microsoft Research, (Redmond, USA), 2012. Title: Fast Learning on Graphs.

    • Conference on Learning Theory (Edinburgh, Scotland) - COLT 2012. Title: A Correlation Clustering Approach to Link Classification in Signed Networks.

    • Conference on Neural Information Processing System (Granada, Spain) - NeurIPS 2011. Title: See the Tree through the Lines: the Shazoo Algorithm (spotlight presentation).

    • International Conference on Machine Learning (Haifa, Israel) - ICML 2010. Title: Random Spanning Trees and the Prediction of Weighted Graphs.

    • Conference on Learning Theory (Haifa, Israel) - COLT 2010. Title: Active Learning on Trees and Graphs.

    • Conference on Algorithmic Learning Theory (Porto, Portugal) - ALT 2009. Title: Learning Unknown Graphs.

    • Conference on Neural Information Processing System (Vancouver, Canada) - NeurIPS 2009 - Workshop: Analyzing Networks and Learning with Graphs. Title: Fast and Optimal Algorithms for Weighted Graph Prediction.

    • Conference on Learning Theory (Montreal, Canada) - COLT 2009. Title: Fast and Optimal Prediction on a Labeled Tree.

    • Conference on Neural Information Processing System (Vancouver, Canada) - NeurIPS 2008 - Workshop: New Challenges in Theoretical Machine Learning: Data Dependent Concept Spaces. Title: Online Graph Prediction with Random Trees.

    • 4th STL-workshop Copenhagen 2003 (Denmark) . Title: Navigation Piles with Applications to Sorting, Priority Queues, and Priority Deques.




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    Miscellaneous


      Research projects / Research group membership

    • 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 Géraud 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.




    Fabio Vitale

      CENTAI Institute

      CENTer for Artificial Intelligence

      Corso Inghilterra 3
      10138 Turin
      Italy






      Previous appointments



      INRIA Lille - Nord Europe (Formerly on leave)

      French Institute for Research in Computer Science and Control

      59650 Villeneuve d'Ascq
      France


      University of Lille (Formerly on leave)

      59653 Villeneuve d'Ascq CEDEX
      France



      MAGNET team (Formerly on leave)

      Machine Learning in information NETworks


      CRISTAL (Formerly on leave)

      Centre de Recherche en Informatique, Signal et Automatique de Lille



      Email:

      fabio.${lastname}@centai.eu

      fabio.${lastname}2@univ-lille.fr

      fabio.${lastname}@inria.fr