Riccardo Cantini

Riccardo Cantini

PhD student in Information and Communication Technologies

DIMES, University of Calabria

Biography

Riccardo Cantini is a PhD student in Information and Communication Technologies at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) of the University of Calabria.
In 2019 he received a Master's degree in Computer Engineering from the University of Calabria and since 2018 he has been a member of the Scalable Computing and Cloud Laboratory (SCALab). Since 2019 he has been a contract professor at the DIMES Department and between 2021 and 2022 he was a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS). He was co-supervisor of several Computer Engineering theses, mainly in the field of social media and big data analysis, machine learning and deep learning.

His research interests include social media and big data analysis, machine and deep learning, sentiment analysis and opinion mining, natural language processing, edge and fog computing, parallel and distributed data analysis.

Education

  • M.Sc. in Computer Engineering, 2019

    University of Calabria

  • B.Sc. in Computer Engineering, 2016

    University of Calabria

Recent Posts

Let's play with MNIST! Generate digits with Convolutional Variational Autoencoders

This post is dedicated to the development of a Flask web application capable of drawing digits through the use of a generative model. This model is obtained by training a convolutional variational autoencoder on the MNIST dataset of handwritten digits using Keras+Tensorflow.
Let's play with MNIST! Generate digits with Convolutional Variational Autoencoders

Personality detection using BERT

In this post I show how to leverage BERT, a transformer-based language representation model, in order to identify the personality type of users based on their writing style and the content of their posts, according to the Myers-Briggs indicator (MBTI).
Personality detection using BERT

Play with BERT! Text classification using Huggingface and Tensorflow

In what follows, I’ll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (negative or positive) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as toxic, severe toxic, obscene, threat, insult and identity hate.
Play with BERT! Text classification using Huggingface and Tensorflow

Recent Publications

Quickly discover relevant content by filtering publications.

Knowledge Discovery From Large Amounts Of Social Media Data

In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, …
Knowledge Discovery From Large Amounts Of Social Media Data

Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups …
Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Programming Big Data Analysis: Principles and Solutions

In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many …
Programming Big Data Analysis: Principles and Solutions

Recent & Upcoming Talks

RTSI 2021

6th online Forum on Research and Technologies for Society and Industry Innovation for a smart world - RTSI 2021
RTSI 2021

Contact

  • riccardo.cantini@unical.it
  • Cubo 41C, 5th floor, via P. Bucci, Rende (CS), Calabria 87036
  • Office hours: send an email for booking an online meeting on Microsoft Teams