Sabrina SENATORE | Computer Science Research Lab

Mission

The activities of the CORE (COmputer Science REsearch) lab focus on the study, analysis and modeling of systems, networks and complex dynamics. The goal of the lab is to develop original methodologies and solutions to face scientific and applicative challenges in several domains such as the network science, network economics, multi-agent systems, computational social choice, data analysis and, more broadly, artificial intelligence.

The laboratory provides a practical and theoretical learning environment to train highly skilled engineers and researchers. It also facilitates collaborations with companies to develop joint research projects. Through the study of complex phenomena and the design of advanced algorithmic solutions, the laboratory seeks to teach methodologies and technologies able to promote economic growth and social welfare.

Activities

The CORE members are involved in several activities in the general domain of the Computer Science and Computer Engineering. Research interests include Game Theory and Computational Economics, Network Science, Artificial Intelligence and Data analysis. Some of the research interests of the CORE members are:

Computational Game Theory and Mechanism Design

In this area, we are interested in the study of strategic interactions among rational agents, with a particular focus on the design of mechanisms that maximize properties such as robustness to manipulations, fairness, social welfare and economic efficiency. Activities in this area are based on consolidated game-theoretic models and techniques (i.e., Nash Equilibria and Strategy-proof mechanisms) and found applications in several real-world domains such as digital markets, network security, resource allocation and social choice. Additionally, research in this area include the design of non-manipulable systems and advancements in auction theory.

Social Network Analysis

This area focuses on the analysis of (social) interactions among members of a network to study complex phenomena such as the dynamics of opinion formation, network formation and evolution, community detection, viral information diffusion in social networks and the identification of important entities and structures in a social network. This is a multidisciplinary area that lies at the intersection of Computer Science, Artificial Intelligence, Operative Research and Economics. Using different models and techniques taken from different domains such as Game Theory, Graph Theory, Theoretical Computer Science, Optimization and Machine Learning, we deal with several problems related to viral information diffusion, opinion manipulation through the social influence and its impact on social choice processes (i.e., elections), viral marketing and complex network analysis.

Research in this area have as numerous applications in real-world domains. Some application domains on which CORE researchers have worked recently are: the contrast to the diffusion of fake news in social networks; the study of election manipulation techniques based on the social influence of the manipulation of the social relationships among the voters; the identification of malicious users in social networks such as Reddit e X (Twitter); analysis of the interactions and the sentiments expressed by the audience of Electronic Sports events (eSports); the impact of critical events (e.g., COVID-19) on the stability of the connections in a social network; the use of viral marketing techniques to incentivize correct and sustainable behaviors, particularly in the public health domain.

Multi-Agent Systems

The study of multi-agent systems focuses on modeling and analyzing the interactions of autonomous and rational agents in shared environments. Research in this area is based on models and techniques from the Evolutionary Game Theory and Cooperative Learning and aim to optimize the coordination, the negotiation and the allocation of resources among the agents. We also simulate real-world scenarios to infer optimal strategies for the agents. This research line has important applications, such as the collaborative robotics, the simulation of social and environmental processes and the design of distributed systems for resource management and monitoring.

Complex Network Science

The analysis of complex networks is based on mathematical and computational methods to explore the structural and dynamical properties of connected systems. In this area we consider both systems that have real direct representations (e.g., social and biological networks) and synthetic systems that are based on some generation process (e.g., Barabási-Albert random networks). It also includes the study of both simple networks and more complex structures, such as hypernetworks. Research interests in this area focus on the study of important aspects such as the resiliency of a network, the automatic learning of the structure of a complex system (e.g., a neural network or a social network) and the identification of influent nodes. Applications of this research span diverse domains, from Social Media to Internet of Things networks.

Knowledge Representation and Reasoning

This area explores advance methodologies and techniques to represent knowledge, making it available and automatically usable to machines. In this area, we use techniques and methods from Artificial Intelligence, such as Logic Programming and Machine Learning, as well as their possible combinations. This research finds application in several domains, such as the extraction of patterns from data using logic formalisms.

Teaching

Researchers of CORE teach or have recently taught several Computer Science classes in the bachelor (L-8) and master (LM-32) programs of Computer Engineering and in the Ph.D. program in Information Engineering of the Department of Information and Electrical Engineering and Applied Mathematics (DIEM). Among these classes there are: Programming Foundations, Computer Networks, Web Design Technologies, Design and Analysis of Algorithms, Security Algorithms and Protocols, Mobile and Distributed Programming, Data Management Systems, Semantic Technologies, Social Networks Analysis, Optimization Techniques for Engineers.

Equipment

We are in the process of acquire:

• 1 graphical workstation with 1 GPU
• 5 PC
• 1 printer