Riccardo Cantini

Researcher (RTDA)

DIMES, University of Calabria

Short Bio

Riccardo Cantini is a researcher (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He graduated magna cum laude in Computer Engineering in 2019, and obtained his Ph.D. in Information and Communication Technologies in 2023, awarded with the Doctor Europaeus label. In 2018 he joined the Scalable Computing and Cloud Laboratory (SCALab), and between 2021 and 2022 he worked as visiting researcher at the Barcelona Supercomputing Center (BSC-CNS). Since 2019 he has been contract professor at the DIMES Department.
His research interests include social media and big data analysis, machine and deep learning, sentiment analysis and opinion mining, natural language processing, topic detection, edge computing, and high-performance data analytics.

Education

Ph.D. Europaeus in Information and Communication Technologies, 2023
University of Calabria
M.Sc. in Computer Engineering, 2019
University of Calabria
B.Sc. in Computer Engineering, 2016
University of Calabria

Recent Publications

Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT

Every day, many people use social media platforms to share information, thoughts, narratives and personal experiences. The vast volume of user-generated content offers valuable …

Riccardo Cantini, Cristian Cosentino, Irene Kilanioti, Fabrizio Marozzo, Domenico Talia

Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT

Programming Big Data Applications: Scalable Tools and Frameworks for Your Needs

In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, …

Domenico Talia, Paolo Trunfio, Fabrizio Marozzo, Loris Belcastro, Riccardo Cantini, Alessio Orsino

DOI

Programming Big Data Applications: Scalable Tools and Frameworks for Your Needs

Deep learning meets smart agriculture: using LSTM networks to handle anomalous and missing sensor data in the compute continuum

In the era of the Internet of Things (IoT), conventional cloud-based solutions struggle to handle the huge amount, high velocity, and heterogeneity of data generated at the network …

Riccardo Cantini, Fabrizio Marozzo, Alessio Orsino

Deep learning meets smart agriculture: using LSTM networks to handle anomalous and missing sensor data in the compute continuum

See all publications