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About the School

Italian Computer Science PhD granting institutions under the auspices of GRIN, organizes an annual school offering three graduate-level courses aimed at PhD students in Computer Science. In addition to introducing students to timely research topics, the school is meant to promote acquaintance and collaboration among young European researchers. The 2022 edition of the School is the 27th in the series.
The school will offer 3 courses:

  • Towards Developmental Machine Learning
    Marco Gori, University of Siena
  • Opinions and conflict in social networks: models, computational problems, and algorithms
    Aristides Gionis, KTH Royal Institute of Technology
  • From Cloud to Serverless through microelements
    Massimo Villari, University of Messina

A final evaluation for each course is possible through a final exam or project as determined by the instructor. The daily schedule admits laboratory and/or working group activities to be organized in addition to the lectures.


Towards developmental Machine Learning

Marco Gori, University of Siena

By and large, most studies of machine learning and pattern recognition are rooted in the framework of statistics. This is primarily due to the way machine learning is traditionally posed, namely by a problem of extraction of regularities from a sample of a probability distribution. This course promotes a truly different way of interpreting the learning of that relies on system dynamics. We promote a view of learning as the outcome of laws of nature that govern the interactions of intelligent agents with their own environment.This leads to an in-depth interpretation of causality along with the definition of the principles and the methods for learning to store events without their long-term forgetting that characterize state of the art technologies in recurrent neural networks. Finally, we reinforce the underlying principle that the acquisition of cognitive skills by learning obeys information-based laws based on variational principles, which hold regardless of biology.

Opinions and conflict in social networks: models, computational problems, and algorithms

Aristides Gionis, KTH Royal Institute of Technology

Online social networks are important venues of public discourse today, hosting the opinions of hundreds of millions of individuals. Social networks are often credited for providing a technological means to break information barriers and promote diversity and democracy. In practice, however, the opposite effect is often observed: users tend to favor content that agrees with their existing world-view, get less exposure to conflicting viewpoints, and eventually create "echo chambers" and increased polarization. Arguably, without any kind of moderation, current social-networking platforms gravitate towards a state in which net-citizens are constantly reinforcing their existing opinions. In this course we present a systematic review of polarization as manifested online, and in particular in online social networks. We start by defining the concept of polarization and reviewing algorithmic methods for detecting, quantifying, and mitigating polarization. Subsequently, we provide an overview of the theory of signed networks, where edges are labeled by a sign, positive or negative. In a social network, where edges might represent interactions between users, the sign may determine whether an exchange was friendly or hostile. This simple modification to the standard graph model gives rise to interesting problem formulations and algorithmic techniques in the context of studying polarization in social networks. Finally, we will discuss models proposed in the literature to explain how individuals form opinions in social networks. We will present the most important opinion-formation models and will discuss some of the computational challenges that have arisen recently.

From Cloud to Serverless through microelements

Massimo Villari, University of Messina

Recent technological advances have disrupted the current centralized cloud computing model, moving cloud resources close to users. Microservice approach allows to instantiate a new paradigm that’s driven by the significant increase in resource capacity/capability at the network fog/edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with datacenter-based services. One of the near future challenges is represented by the management of even more high distributed systems along with the need to federate those environments. The automatic deployment of microservices is becoming a must, importantly they have to be composed and interconnected over IoT, Fog, edge and cloud infrastructures, adopting the Serverless paradigm, and also thinking to Security from the beginning. Various stakeholders (Cloud providers, Edge providers, Fog provider, Security provider, Application providers, IoT, DevOPs, and so on) can contribute to the provisioning of new Serverless and FaaS applications in Federated Environments. Osmotic Computing relies on microelements, an extension of microservices that Serverless can benefit of, for smoothly managing challenging services of the future.


Towards developmental Machine Learning (Gori)
Opinions and conflict in social networks: models, computational problems, and algorithms (Gionis)
From Cloud to Serverless through microelements (Villari)

09.00-10.30 Villari Villari Villari Villari Villari
10.30-11.00 Coffee break
11.00-12.30 Gori Gori Gori Gori Gori
12.30-14.00 Lunch break
14.00-15.30 Gionis Gionis Gionis Gionis Gionis


Scientific Organizing Committee

Paolo Boldi, University of Milano
Antonio Brogi, University of Pisa
Maurizio Gabbrielli, University of Bologna

Local Organization

Andrea Bandini, CeUB
Monica Michelacci, CeUB
Stefano P. Zingaro, UniBO
Saverio Giallorenzo, UniBO


The school will be held online and the registration fee is 100.00 Euro.

In order to register, all applicants must fill the form available at the following link: REGISTRATION FORM.

Past Editions

BISS 2019:

  • Internet of things: a data oriented approach
  • Multitask learning and learning-to-learn: a statistical learning perspective
  • Software security across abstraction layers

BISS 2018:

  • Provable security for low level execution platforms
  • Distributed models, MapReduce and large scale algorithms
  • Elements of Quantum Computation

BISS 2017:

  • Approximation Algorithms
  • Probabilistic Graphical Models in Intelligent Systems
  • Kleene algebra with tests and applications to network programming

BISS 2016:

  • Advanced Topics in Programming Languages
  • Model and Languages for Service-Oriented and Cloud Computing
  • Algorithms and Methods for Mining Large Graphs

BISS 2015:

  • Game Theory: Models, Numerical Methods, and Applications
  • Protection of sensitive information
  • Introduction to Modern Cryptography

BISS 2014:

  • Big Data Analysis of Patterns in Media Content
  • An Introduction to Probabilistic and Quantum Programming
  • Development of dynamically evolving and self-adaptive software

BISS 2013:

  • Foundations of Security: Cryptography, Protocols, Trust
  • Stochastic Process Algebras for Quantitative Analysis
  • Shape and Visual Apperance Acquisition for Photo-realistic Visualization

BISS 2012:

  • Algorithms for the web and for social networks
  • Software Verification and Interactive Theorem Proving
  • Regularization methods for high dimensional learning

BISS 2011:

  • Computational Aspects of Game Theory
  • Trust in Anonymity Networks (TAN)
  • Information Integration (II)
  • Model Checking: From Finite-state to Infinite-state Systems (MCFIS)