Research interest

I am Cristian Cosentino, a PhD student (PhD) in ICT at DIMES at the University of Calabria. My email is verified at dimes.unical.it, and I have a web page dedicated to my academic and research activities. My main areas of expertise include big data analytics, social media analytics, machine learning, deep learning, and natural language processing.

Among my most relevant publications are articles and studies that have gained recognition in the field. For example, I contributed to the improvement of cryptocurrency price forecasting by integrating machine learning with social media and market data, published in “Algorithms” in 2023. In addition, I participated in research unmasking false information about COVID-19 on Twitter using a BERT-based approach, presented at the International Conference on Discovery Science in 2023. Another publication of mine in 2024 explores multi-dimensional classification of social media data using large-scale language models.

In addition to my research activities, I also conduct teaching activities as a tutor in several subjects, including computer networks, machine learning and data analysis, contributing to the training of students in these advanced disciplines.

If you are a student interested in doing your thesis with us, or a researcher eager to collaborate, please know that in addition to our specialization in networks, we are involved in a number of other areas. These include the use of large language models (LLMs) for social media analysis, including Amazon, TripAdvisor and Twitter. In addition, we are actively engaged in the field of machine learning and deep learning, closely examining current research contexts and technological innovations available in the market.

Participation in research projects

INSIDER: INtelligent ServIce Deployment for advanced cloud-Edge integRation

The INSIDER project explores novel solutions to advance the state of the art toward more effective use of hybrid cloud/edge infrastructures for IoT applications. In particular, it aims at defining novel adaptive solutions for finding the most suitable deployment of the services of an application between cloud and edge, so as to meet both functional and non-functional application requirements. INSIDER will provide automatic tools to explore the large number of alternative deployment configurations and identify the best one(s) according to infrastructure constraints and service requirements. The main result of this project is the definition and development of an intelligent framework for analyzing, supporting, and deploying IoT applications for hybrid cloud/edge infrastructures. This project has received funding from the Italian Ministry of University and Research, PRIN 2022 “INSIDER: INtelligent ServIce Deployment for advanced cloud-Edge integRation”, grant n. 2022WWSCRR, CUP H53D23003670006.