Our field of study

Adversarial Machine Learning

Adversarial Machine Learning

Artificial Intelligence and Machine Learning

  •        Cognitive computing and machine intelligence
  •        Recommender Systems
  •        Machine/Deep learning algorithms and applications
  •        Semantic Web and Linked Open Data
  •        Ontologies
  •        Knowledge representation and automated reasoning
  •        User modeling and Preference reasoning

Business Process Management

  • Models and architectures based on the abstraction of processes, services and events
  • Business Processes Modeling and Management
  • Semantic Business Process
  • Model-driven approaches
  • Service engineering
  • Complex event processing and event-driven architectures

Conversational Agent

Converationa Agent

Cyber-Physical and Mobile Systems, Ubiquitous Web

  •        Wireless Sensor and Actor Networks
  •        Smart Supply Chains/Grids
  •        Domotics and Smart Homes
  •        Vehicular Networks
  •        Mobile GUIs
  •        Application centric Protocols

Edge Computing

Edge Computing

Explainable AI (XAI)

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision.[1] XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal rights or regulatory requirements—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions.

Federated Learning


Formal Verification and Model Checking

  •        Formal methods
  •        Temporal logics and model checking
  •        Runtime verification 

Information Systems

  •        Cloud computing
  •        Web Information Systems
  •        Distributed Systems
  •        Big Data Analysis

Knowledge Graphs

Knowledge Graphs

Medical Informatics and Health Information Technology

Medical Informatics and Health Information Technology

Natural Language Processing

Natural Language Processing (NLP) is a subfield of linguistics, computer science, information engineering and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data.

Recommender Systems

A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.

Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts, collaborators, jokes, restaurants, garments, financial services, life insurance, romantic partners, and Twitter pages.

Software Architecture

  • Linking architecture to requirements and/or implementation
  • Architecture verification, validation and evaluation
  • Architectural patterns, styles and tactics, reference architectures
  • Component-based models and deployment, middleware
  • Architectures for Systems of Systems, IoT system, cyber-physical systems
  • Architectures for reconfigurable and self-adaptive systems

Software Engineering

  • Requirements engineering
  • Software modeling, design and testing
  • Software testing
  • Software engineering management
  • Software development process
  • Model-driven engineering
  • Architecting software ecosystems
  • Microservices architecture
  • Complex event processing

SisInf Lab - Information Systems Laboratory

Research group of Politecnico di Bari
Edoardo Orabona St, 4 Bari, Italy