Every day, millions of people browse the web, watch videos, search for information, and increasingly interact with generative AI tools such as ChatGPT, Gemini, and Claude. While these technologies have become essential parts of our personal and professional lives, their environmental impact often remains invisible. Most users are aware that activities such as driving a … Continue reading Empowering Users toward Environmentally Sustainable Digital Practices: Web Apps and Generative AI
In aerospace engineering, ensuring that electronic components are properly qualified is essential for safety and mission success. However, in large organizations, qualification data is often fragmented across multiple systems, making retrieval slow, error-prone, and expensive. In this story, I would like to report on our study in the industrial context of Thales Alenia Space that … Continue reading Building Scalable Data Integration in Aerospace with LLMs and Knowledge Graphs
Our recent research on the role of individuals who do not comply with public health safety measures in epidemic context has been published in the Proceedings of the Royal Society A. The study shows how risky behaviours in case of pandemic and epidemic settings can undermine public health interventions. This is particularly relevant in urban … Continue reading A data-driven analysis of the impact of non-compliant individuals on epidemic diffusion in urban settings
In this work, we investigate the use of Large Language Models (LLMs) within a Graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering.First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the energy field. Then, the generated graph is navigated and reasoned upon to provide users … Continue reading A Graph-based RAG for Energy Efficiency Question Answering
Within the context of our Data Science Lab and research team, we offer a variety of thesis options. Check them out in these slide decks: https://www.slideshare.net/slideshow/embed_code/key/44O3NXu3iERiyS?hostedIn=slideshare&page=upload https://www.slideshare.net/slideshow/embed_code/key/xg4X4dr14ySsWt?hostedIn=slideshare&page=upload
We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks.
Five online courses (MOOCs) that collect technical and policy solutions to pandemic challenges have been published on Coursera
I've been invited to give a keynote talk at the WISE 2022 Conference. Thinking about it, I decided to focus on my idea of a bi-verse. To me, the bi-verse is the duality between the physical and digital worlds. On one side, the Web and social media are the environments where people post their content, … Continue reading Exploring the bi-verse: a trip across the digital and physical ecospheres
We published a review article that aims to present a literature overview on collecting and employing human knowledge to improve and evaluate the understandability of machine learning models through human-in-the-loop approaches.
Despite the increasing limitations for unvaccinated people, in many European countries, there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. Within the PERISCOPE project, we studied the role of online social media in influencing individuals' opinions about getting vaccinated by designing a … Continue reading The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
