Arco Lab
ArCo at Università Campus Bio-Medico di Roma specializes in artificial intelligence research, with applications spanning medicine, industrial and environmental monitoring, energy management, and digital twins. Established in 2004, the lab develops AI-driven methodologies including multimodal learning, AI resilience, and computer vision, with a strong focus on oncology, connected health, medical records, and biomedical signals.
Research Areas
Research areas from intelligent methods to real-world translation
Artificial Intelligence
We develop AI methods for multimodal data, generative modelling, time-series analysis, and decision support, with attention to robustness, interpretability, and explainability.
Computer Systems
We design and optimize computing systems that bring intelligence close to sensors and devices, including embedded platforms, IoT architectures, edge computing, and performance-aware AI deployment.
Control & Dynamical Systems
We develop control and estimation methods for networked and dynamical systems, including distributed filtering, consensus algorithms, nonlinear dynamics modelling, and adaptive biomedical control.
Applications
We apply AI and system-level methods across healthcare, industrial processes, agriculture, and data-intensive domains, focusing on reliability, interpretability, and measurable real-world impact.
Translation
We support the translation of research into operational solutions, working with companies, institutions, and clinical partners to prototype, validate, and deploy data-driven technologies.
Active Research Projects
Cyber ACN
Sicurezza dei dati medici: strumenti di Intelligenza Artificiale Generativa per la condivisione e l'anonimizzazione sicura dei dati
Open project page
LUMINATE
Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization
Open project page
Meet the Research Group
ArCo Lab is built on multidisciplinary collaboration among computer engineers, biomedical engineers, data scientists, postdocs, PhD students, researchers, and professors, combining methodological depth with clinical and applied perspectives.
Open Team Page
Recent Publications
- Sensor-Based Assessment of Plant–Microbe Energy Interactions2026generative AI medical imaging
- A noninvasive strategy for multi-disease diagnosis via multi-sensor platform: integrative analysis of five years of exhaled breath–based diagnostics for seven diseases2026clinical prediction medical imaging
- Beyond a single mode: GAN ensembles for diverse medical data generation2026generative AI medical imaging