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Who am i?
Who am i?
My name is Giorgio Barnabò.
At present, I am pursuing a Ph.D. in Data Science at Sapienza University of Rome and at Pompeu Fabra University of Barcellona.
Carlos Castillo and Fabrizio Silvestri are my supervisors, and we have been working on Online Active Learning for Fake News Detection Using GNNs.
I am also strongly interested in Cultural Analytics, Computational Social Science and Automatic Music Generation.
I am a member of the Sapienza School for Advanced Studies.
I am proficient in Python, Pythorch, R, Spark, SQL, SPARQL
I am fluent in Italian, English, French, and Spanish
EDUCATION
EDUCATION
(September 2018 up to present) PhD student in Data Science at the Department of Computer, Control, and Management Engineering of Sapienza University of Rome (supervisors: Stefano Leonardi & Carlos Castillo);
member of the SSAS (Sapienza School for Advanced Studies);
(October 2016 - October 2018) Master Degree in “Statistics and Decision Science” at the Statistics Department of La Sapienza University of Rome; (degree grade: 110/110 cum laude);
member of the SSAS (Sapienza School for Advanced Studies);
(September 2017 – June 2018) Erasmus Exchange at the Paris Dauphine University (FR);
(October 2012 – October 2015) Bachelor Degree in “Statistics, Economics and Finance” at the Statistics Department of La Sapienza University of Rome (degree grade: 110/110 cum laude);
(January – July 2015) Erasmus Exchange at the Southampton University (UK)
PEER REVIEWED PUBLICATIONS
PEER REVIEWED PUBLICATIONS
Vitiugin, F., & Barnabo, G. (2021). Emotion Detection for Spanish by Combining LASER Embeddings, Topic Information, and Offense Features. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021). CEUR Workshop Proceedings, CEUR-WS, Málaga, Spain.
We propose a novel model for emotion detection that combines transformers embeddings with topic information and offense features.
Mathioudakis, M., Castillo, C., Barnabo, G., & Celis, S. (2020, March). Affirmative action policies for top-k candidates selection: with an application to the design of policies for university admissions. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 440-449).
We consider the problem of designing affirmative action policies for selecting the top-k candidates from a pool of applicants. We use a causal framework to describe and analyse several families of policies whose goal is to increase the selection of people from disadvantaged socio-demographic groups.
Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, and Chris Schwiegelshohn. 2019. Algorithms for Fair Team Formation in Online Labour Marketplaces✱. In Companion Proceedings of The 2019 World Wide Web Conference (WWW '19), Ling Liu and Ryen White (Eds.). ACM, New York, NY, USA, 484-490.
We define the Fair Team Formation problem where the goal is to find a team with all the skills needed to complete a given task, and that has the same number of people from all protected classes. To solve the problem we propose four different algorithms with approximation guarantees.
PRE-Prints
PRE-Prints
Barnabò, Giorgio, Giovanni Trappolini, Lorenzo Lastilla, Cesare Campagnano, Angela Fan, Fabio Petroni, and Fabrizio Silvestri. "CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN." arXiv preprint arXiv:2104.00353 (2021).
In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN.
Barnabo, G., Castillo, C., Mathioudakis, M., & Celis, S. (2020). Intersectional Affirmative Action Policies for Top-k Candidates Selection. arXiv preprint arXiv:2007.14775.
We study the problem of designing affirmative action policy to reduce acceptance rate disparities in the selection process of top-k candidates from a pool of applicants, while avoiding any large decrease in the aptitude of the candidates that are eventually selected.
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