Università degli Studi di Milano

Welcome!

The Industrial, Environmental and Biometric Informatics Laboratory (IEBI Lab) is based at the Department of Computer Science of the Università degli Studi di Milano, Milan, Italy.

The laboratory conducts research in artificial intelligence, machine learning, computational intelligence, signal and image processing, biometric systems, instrumentation and measurement, and industrial informatics. Its activities focus particularly on the development of intelligent methods for industrial monitoring, environmental monitoring, biometrics, and medical applications.

IEBI Lab members are also actively involved in teaching and supervision activities within the degree programmes of the Università degli Studi di Milano.

This website presents the laboratory’s members, publications, research projects, and ongoing scientific activities.

Explore projects

The laboratory is a member of the following international research networks:

Research areas

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Industrial informatics

Computer vision, machine learning, and intelligent sensing for automated inspection, defect detection, quality control, process monitoring, predictive maintenance, and decision support in industrial environments.

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Environmental informatics

Artificial intelligence, image analysis, and data-driven models for environmental observation, energy forecasting, autonomous mobility, natural-resource monitoring, and the detection of critical events.

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Biometric systems

Advanced image and signal processing for identity recognition, contactless biometrics, presentation-attack detection, biometric privacy, template protection, and secure matching.

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Medical imaging

Image processing and machine learning for medical-image analysis, computer-aided diagnosis, segmentation, classification, explainable decision support, and the analysis of microscopy and clinical imaging data.

Featured projects

All projects

Industrial informatics

Anomaly Detection

The project develops image-based anomaly detection methods for industrial quality control, with particular attention to scarce defective data, explainability, and distributed deployment. It combines synthetic defect generation, guided-attention architectures, and lightweight diffusion models to support anomaly classification, localization, and segmentation. Experimental results on MVTec AD show that generated defects improve recognition of real anomalies, while the proposed models provide competitive accuracy with reduced reliance on external datasets and large pretrained architectures.

Industrial informatics

Edge AI Technologies for Optimised Performance Embedded Processing

EdgeAI develops secure, scalable, energy-efficient, and trustworthy artificial-intelligence technologies for deployment across the edge continuum, from embedded devices to distributed edge infrastructures. The project integrates hardware/software co-design, model optimisation, explainability, and real-time processing in industrial demonstrators spanning digital industry, energy, agri-food, mobility, and digital society.

Medical imaging

Leukemia Detection

Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is a disease that affects the blood cells, can spread rapidly throughout the body, and may result in fatal consequences if not detected at an early stage. One of the techniques routinely used to diagnose ALL consists in analyzing White Blood Cells (WBC) present in peripheral blood samples to look for malformations or abnormalities. Such malformations may be an indicator of lymphoblasts, which naturally occur in the bone marrow. However, an elevated number of WBCs with lymphoblast characteristics may be a sign of ALL.