Gianluca Aguzzi

PhD Student


I am a PhD student in Computer Science at the Department of Computer Science and Engineering of the University of Bologna (Italy). My main research interests include software engineering, pervasive systems, and multi-agent reinforcement learning. Particularly, In my PhD, I will follow a language-based engineering approach for Cyber-Physical Swarm—multi-agent systems in which system-wide goals are pursued with collective behaviours. My research ranges from multi-agent learning, distributed algorithms and engineering methodologies, with the goal of finding a systematic procedure to synthesise and deploy self-organising behaviours of predictable outcomes for CPSW. In this direction, I explore a collective reinforcement learning approach for program sketching and a distributed and reinforcement-based distributed scheduler for aggregate computing. For more details, please look at bibliography.

Work Experience

Tutor @ Paradigmi di Progettazione e Sviluppo (PPS)

Università di Bologna -- ALMA MATER STUDIORUM | 2022

Tutor @ Concurrent & Distributed Programming (PCD)

Università di Bologna -- ALMA MATER STUDIORUM | 2022

Teacher the coding courses

Criad Coding | 2019 - 2019

As a primary school teacher, I taught children the main computational thinking, namely a set of problem-solving methods that involve expressing problems and their solutions in ways that a computer could also execute. It was such a great experience in which I improved my speaking and storytelling capabilities.

Tutor in coding training courses for teachers

Criad Coding | 2019 - 2019

In this experience, I paired a professional teacher in classes composed of people with a different background and of a different age. This work helps me to improve my improvisation and teamwork capabilities

International Conference

Student Volunteer @ ACSOS 2022


Student Volunteer @ ICDCS 2022


Artefact Evaluation Committe @ COORDINATION 2022


Artefact Evaluation Committe @ ACSOS 2021



ScaFi Web

Open Source

ScaFi Web ( is an online playground used to facilitate aggregate programming (i.e. a novel way to program collective adaptive systems, my area of interest). It was my master thesis work and then it was refined for research reason. Currently, the system is hosted on



ScaFi ( is a toolkit for developing Aggregate Computing applications.


Aguzzi, Audrito, Casadei, Damiani, Torta & Viroli (2023)
, , , , & (). A field-based computing approach to sensing-driven clustering in robot swarms. Swarm Intell., 17(1). 27–62.
Aguzzi, Casadei, Pianini & Viroli (2022)
, , & (). Dynamic decentralization domains for the internet of things. IEEE Internet Comput., 26(6). 16–23.
Casadei, Viroli, Aguzzi & Pianini (2022)
, , & (). ScaFi: A scala DSL and toolkit for aggregate programming. SoftwareX, 20. 101248.
Aguzzi, Casadei & Viroli (2022)
, & (). Addressing collective computations efficiency: Towards a platform-level reinforcement learning approach. IEEE.
Aguzzi, Casadei & Viroli (2022)
, & (). Towards reinforcement learning-based aggregate computing. Springer.\_5
Casadei, Pianini, Aguzzi, Audrito, Torta, Ottina, Damiani & Viroli (2022)
, , , , , , & (). Towards automated engineering for collective adaptive systems: Vision and research directions. IEEE.
Aguzzi, Casadei & Viroli (2022)
, & (). Machine learning for aggregate computing: A research roadmap. IEEE.
Aguzzi, Casadei, Pianini & Viroli (2022)
, , & (). Dynamic decentralization domains for the internet of things - simulation repository.; IEEE DataPort.
Casadei, Aguzzi & Viroli (2021)
, & (). A programming approach to collective autonomy. J. Sens. Actuator Networks, 10(2). 27.
Aguzzi, Casadei, Pianini, Salvaneschi & Viroli (2021)
, , , & (). Towards pulverised architectures for collective adaptive systems through multi-tier programming. IEEE.
Aguzzi (2021)
(). Research directions for aggregate computing with machine learning. IEEE.
Aguzzi, Casadei, Maltoni, Pianini & Viroli (2021)
, , , & (). ScaFi-web: A web-based application for field-based coordination programming. Springer.\_18
Delnevo, Aguzzi, Letizi, Luffarelli, Petreti & Mirri (2021)
, , , , & (). Encouraging users in waste sorting using deep neural networks and gamification. ACM.