Research Programs

Deep and Structured Machine Learning

This research area addresses the development of machine learning techniques and systems for reasoning over complex domains involving multiple entities, relationships and constraints. The complexity of the information to be processed and of the tasks to be addressed calls for the design of elaborate architectures and advances in fields such as deep and statistical relational learning, structured prediction, multi-task learning, etc. Prototypical and main application domains are Computer Vision and Multimedia, Natural Language Processing, and Bioinformatics.

Research areas

  • Bioinformatics
  • Computer vision
  • Human behaviour understanding
  • Information retrieval
  • Learning with constraints
  • Machine learning
  • Multimedia analysis
  • Natural language processing
  • Statistical relational learning


Ongoing projects

ACANTO: A CyberphysicAl social NeTwOrk using robot friends (H2020)

xLiMe: CrossLingual crossMedia knowledge extraction (FP7)

QoSTREAM: Video Quality Driven Multimedia Streaming in Mobile Wireless Networks (FP7 Marie Curie Action)

AAH: Active Ageing at Home, Tecnologie per gli Ambienti di Vita  (Cluster MIUR)

CogNet: Building an Intelligent System of Insights and Action for 5G Network Management  (H2020)

Combining neural networks and convolution kernels (Google project)

Protein Function Prediction by Statistical Relational Learning (Google Faculty Research Award)

La knowledge base del Trentino: Ragionamento semantico-probabilistico su larga scala per i dati della Pubblica Amministrazione (CARITRO) 



Faculty Members

Fausto GiunchigliaAlessandro MoschittiAndrea Passerini (Coord)
Elisa RicciEnver SanginetoNicu Sebe

Further Information

Technical reports of Research Program
Published papers of Research Program