Email Github Linkedin Scholar

Alessio Gravina​ is a Ph.D. student in Computer Science at the University of Pisa, supervised by Davide Bacciu. His research interests take place in the domain of Machine Learning and Graph Representation Learning, with particular attention to problems with positive impact on society. He is a member of Computational Intelligence and Machine Learning group and Pervasive AI Lab. Alessio during his career won the Fujistu AI-NLP challenge, and he spent five months at University College Dublin as a member of the Erasmus+ programme, and three months at Stanford University researching machine learning techniques for Schizophrenia treatment.

Interests: Machine Learning for Graphs, Deep Learning, Dynamic Graphs

Download CV (49 KiB)

This website has been designed to minimise the energy consumption and CO2 emissions that result from navigating the internet. The interface uses Arial and Times New Roman to avoid unnecessary HTTP requests. It is available only in dark mode to reduce screen brightness and energy consumption – especially in mobile use where OLED screens are most common.


Location University of Pisa
From - To 11/2020 - ongoing
Description Representation Learning for Dynamic Graphs

Location Virtual
From - To 08/2021
Description 15-days specialized AI school that covers the topics of Rep. Learning & Statistical ML, ML in Healthcare, NLP, and AI for Good. Acceptance rate 15%.

Location University College Dublin
From - To 01/2019 - 05/2019
Description Student at the Computer Science Department in the framework of the EU Erasmus+ project

Location University of Pisa
From - To 09/2018 - 03/2020
Final grade 110/110 cum laude (equivalent GPA: 4/4)
Thesis title Machine Learning prediction of compounds impact on Schizophrenia treatment

Location University of Pisa
From - To 09/2014 - 03/2018
Final grade 103/110 (equivalent GPA: 3.75/4)
Thesis title Machine Learning for the prediction of Bronchopulmonary dysplasia risk


Location Dalle Molle Institute for Artificial Intelligence Research (IDSIA USI-SUPSI)
From - To 04/2022 - 07/2022
Description Worked on Representation Learning for Dynamic Graphs under the supervision of Prof. Cesare Alippi

Location University of Pisa
From - To 02/2021 - 05/2021
Course Introduction to Programming and Algorithms
Description Weekly office hours for homework assistance and reinforcement of learned concepts

Location University of Pisa
From - To 07/2020 - 11/2020
Description Worked on Deep Learning for graphs applied to Covid-19 related data

Location Vydiant
From - To 01/2020 - 06/2020
Description Worked on relation identification for biomedical corpus

Location Stanford University
From - To 09/2019 - 12/2019
Description Worked on Deep Learning for graphs applied to Schizophrenia treatment


Rank 1st place
Prize $20,000
Year 2018
Description Developed a novel natural language processing technology to complement and strengthen Fujitsu’s Zinrai FAQ search.


(*) means corresponding author or equal contribution

  author = {Alessio Gravina and Davide Bacciu and Claudio Gallicchio},
  title = {Anti-Symmetric {DGN}: a stable architecture for Deep Graph Networks},
  booktitle = {The Eleventh International Conference on Learning Representations },
  year = {2023},
  url = {}
Abstract Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in their ability to propagate and preserve long-term dependencies between nodes, i.e., they suffer from the over-squashing phenomena. This reduces their effectiveness, since predictive problems may require to capture interactions at different, and possibly large, radii in order to be effectively solved. In this work, we present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations. We give theoretical proof that our method is stable and non-dissipative, leading to two key results: long-range information between nodes is preserved, and no gradient vanishing or explosion occurs in training. We empirically validate the proposed approach on several graph benchmarks, showing that A-DGN leads to improved performance and enables to learn effectively even when dozens of layers are used.

   author = {Bacciu, Davide and Errica, Federico and Gravina, Alessio and Madeddu, Lorenzo and Podda, Marco and Stilo, Giovanni},
   title = {Deep Graph Networks for Drug Repurposing with Multi-Protein Targets},
   journal = {IEEE Transactions on Emerging Topics in Computing},
   year = {2023},
Abstract In the early phases of the COVID-19 pandemic, repurposing of drugs approved for use in other diseases helped counteract the aggressiveness of the virus. Therefore, the availability of effective and flexible methodologies to speed up and prioritize the repurposing process is fundamental to tackle present and future challenges to worldwide health. This work addresses the problem of drug repurposing through the lens of deep learning for graphs, by designing an architecture that exploits both structural and biological information to propose a reduced set of drugs that may be effective against an unknown disease. Our main contribution is a method to repurpose a drug against multiple proteins, rather than the most common single-drug/single-protein setting. The method leverages graph embeddings to encode the relevant proteins' and drugs' information based on gene ontology data and structural similarities. Finally, we publicly release a comprehensive and unified data repository for graph-based analysis to foster further studies on COVID-19 and drug repurposing. We empirically validate the proposed approach in a general drug repurposing setting, showing that it generalizes better than single protein repurposing schemes. We conclude the manuscript with an exemplified application of our method to the COVID-19 use case. All source code is publicly available.

  doi = {10.1371/journal.pcbi.1009531},
  author = {Gravina, Alessio AND Wilson, Jennifer L. AND Bacciu, Davide AND Grimes, Kevin J. AND Priami, Corrado},
  journal = {PLOS Computational Biology},
  publisher = {Public Library of Science},
  title = {Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks},
  year = {2022},
  month = {05},
  volume = {18},
  url = {},
  pages = {1-19},
  number = {5},                
Abstract Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.

  author    = {Gravina, Alessio and Rossetto, Federico and Severini, Silvia and Attardi, Giuseppe},
  editor    = {Bernardi, Raffaella and Navigli, Roberto and Semeraro, Giovanni},
  title     = {A Comparative Study of Models for Answer Sentence Selection},
  booktitle = {Proceedings of the Sixth Italian Conference on Computational Linguistics,
              Bari, Italy, November 13-15, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2481},
  publisher = {},
  year      = {2019},
  url       = {}
Abstract Answer Sentence Selection is one of the steps typically involved in Question Answering. Question Answering is considered a hard task for natural language processing systems, since full solutions would require both natural language understanding and inference abilities. In this paper, we explore how the state of the art in answer selection has improved recently, comparing two of the best proposed models for tackling the problem: the Crossattentive Convolutional Network and the BERT model. The experiments are carried out on two datasets, WikiQA and SelQA, both created for and used in open-domain question answering challenges. We also report on cross domain experiments with the two datasets.

  author    = {Gravina, Alessio and Rossetto, Federico and Severini, Silvia and Attardi, Giuseppe},
  editor    = {Basile, Pierpaolo and Basile, Valerio and Croce, Danilo and Dell'Orletta, Felice and Guerini, Marco},
  title     = {Cross Attention for Selection-based Question Answering},
  booktitle = {Proceedings of the 2nd Workshop on Natural Language for Artificial
                Intelligence {(NL4AI} 2018) co-located with 17th International Conference
                of the Italian Association for Artificial Intelligence (AI*IA 2018),
                Trento, Italy, November 22nd to 23rd, 2018},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2244},
  pages     = {53--62},
  publisher = {},
  year      = {2018},
  url       = {\_05.pdf},
Abstract Answer Sentence Selection (ASS) is one of the steps typically involved in Question Answering, a hard task for natural language processing since full solutions would require both natural language understanding and world knowledge. We present a new approach to tackle ASS, based on a Cross-Attentive Convolutional Neural Network. The approach was designed for competing in the Fujitsu AI-NLP challenge Fujitsu [4], which evaluates systems on their performance on the SelQA [7] dataset. This dataset was created on purpose as a benchmark to stress the ability of systems to go beyond simple word co-occurrence criteria. Our submission achieved the top score in the challenge.


Dipartimento di Informatica, Largo B. Pontecorvo 3, 56127 Pisa, Italy