Riccardo Cantini

Riccardo Cantini

Assistant Professor (RTDA)

DIMES, University of Calabria

Short Bio

Riccardo Cantini is an assistant professor (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 European Ph.D. in Information and Communication Technologies in 2023. 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).
His research interests include social media and Big Data analysis, opinion mining, Natural Language Processing, Large Language Models, and sustainable AI.

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

XAI-driven Knowledge Distillation of Large Language Models for Efficient Deployment on Low-Resource Devices

Large Language Models (LLMs) are characterized by their inherent memory inefficiency and compute-intensive nature, making them impractical to run on low-resource devices and …
XAI-driven Knowledge Distillation of Large Language Models for Efficient Deployment on Low-Resource Devices

Detecting mental disorder on social media: a ChatGPT-augmented explainable approach

In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This …
Detecting mental disorder on social media: a ChatGPT-augmented explainable approach

Block size estimation for data partitioning in HPC applications using machine learning techniques

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In …
Block size estimation for data partitioning in HPC applications using machine learning techniques

Books

book-cover

Programming Big Data Applications
Scalable Tools and Frameworks for Your Needs

Contact

  • riccardo.cantini@unical.it
  • Cubo 41C, 5th floor, via P. Bucci, Rende (CS), Calabria 87036
  • Office hours: send an email or contact me via Microsoft Teams to request a meeting