Università degli Studi di Salerno, Fisciano (SA) -- 84084 🇮🇹
Currently, he is a Tenured Assistant Professor at UNISA, and he is a senior member of ISISLab laboratory.
He got his MSc and Ph.D. in Computer Science at the UNISA in 2013 and 2017, respectively, under the supervision of Prof. Vittorio Scarano and Prof. Gennaro Cordasco.
He is interested in parallel algorithms, distributed systems, graph theory, network science, and agent-based simulations.
In 2012, he got a grant from the Office of Naval Research (ONR) to visit George Mason University (GMU). In May 2017 and from October to December 2017, he was a visiting student at the University of Chicago and Argonne National Laboratory (ANL) under the supervision of Jonathan Ozik and exploiting a grant from ANL. In December 2019, he was a visiting researcher at GMU under the supervision of Prof. Sean Luke.
Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.