Artificial Intelligence And Big Data Laboratory

About us

The Artificial Intelligence and Big Data Laboratory is mainly devoted to the design, analysis and experimentation of algorithms and systems involving Big structured and unstructured Data for the Web and for IoT, Mobile and Pervasive computing, Activity recognition and Machine Learning in order to tackle different research topics in various domains: Recommendation Systems, Semantic Web, Text Classification and Extraction, Graph Theory and analysis, Data Mining, Artificial Intelligence, Computer Vision, Big Data, Machine Learning, Deep Learning, Natural Language Processing, Human Robot Interaction and Human activity identification based on sensor data. Some examples of research applications where we have applied the technologies above are:

  • Traffic road anomalies and accident identification and prevention
  • Recommender systems, social media analysis, behavioural pattern identification
  • Credit Scoring, Fraud Detection and Intrusion Detection
  • Financial forecasting and robo-trading
  • Sentiment Analysis
  • Robotic Applications
  • E-coaching platforms for well being and ageing

The Laboratory is very dynamic and open to collaborate with national and international partners. It has a strong orientation to the market and the exploitation of the research results. The laboratory has several projects with business institutions for tasks related to the technological transfer. It has experience with different national and European programs (e.g. FP7, H2020).

Besides students and researchers from the University of Cagliari, the Laboratory includes also external collaborators from different institutions. International students or researchers willing to start a collaboration with us are welcome to contact us using the related contact form. Although the computer science sphere is still well balanced as far as the genre dimension is concerned, we try to foster gender balance in research teams as we believe that integrating gender dimension in research and innovation content helps improving the scientific quality and societal relevance of the produced knowledge, technology and innovation.