PRAISELab@PICUSLab

university

AI & ML interests

Artificial Intelligence, Generative AI, Large Language Models, Biomedical NLP, Medical Question Answering, Generative Agent-based Modeling, Recommender System, Social Networks

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Hi 👋, we are the PRAISE Research Group

Predictive Analytics for Understanding Big Multimedia Data @ University of Naples Federico II

🧠 About Us

The PRAISE (PRedictive AnalytIcs for underStanding big multimEdia data) research group is part of the PICUS Lab at the Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II, Italy.
We are a team of researchers with a strong background in AI, multimodal data understanding, and predictive analytics across multiple domains including eHealth, Social Network Analysis, and Multimedia Data Analysis.

Our goal is to develop intelligent systems that can understand, predict, and interact with complex multimedia information—especially focusing on language, vision, and their integration.

🔬 Research Topics

Multimodal AI for Healthcare
- Multilingual and multimodal language models
- Diagnostic and therapeutic AI assistance
- Automatic EHR generation from speech and unstructured text (Italian)
- Medical image understanding (CT, MRI, X-ray, etc.)
Social Network Modeling & Simulation
- Generative Agent-Based Frameworks
- LLM-driven simulation of social phenomena: stance change, information diffusion
- Tools for crowd fact-checking, fake news and harmful content detection
Lie Detection from Multimedia Streams
- AI for behavioral analysis and deception detection
- Collaborative project with US-based Courtscribes Company
Big Scholarly Data Analytics
- Automated knowledge extraction and semantic enrichment
- Development of analytics platforms for scientific literature

🤖 Models

We are currently developing fine-tuned multimodal language models for the Italian medical domain, in collaboration with national (e.g., Accenture, Almaviva) and international partners (e.g., King’s College London).
Our models integrate textual and visual medical data to support clinical tasks in real-world applications.