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The Cluster Network

CIML Group

Computational Intelligence & Machine Learning Group - UNIPI

  • Pharma/Biopharma
  • ICT for Health / e-health
  • Medical devices/Biotech (non pharma)

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Department: 
Dipartimento di Informatica
Public research organization: 
Università degli Studi di Pisa
Web site :
http://www.di.unipi.it/groups/ciml/
VAT Number: 
00286820501
Address:
Largo B. Pontecorvo, 3 - 56127 - Pisa
 micheli@di.unipi.it
 +39 050 2212700

Overview: 

CIML group addresses the development of new Artificial Intelligence methodologies and the analysis of their computational properties , based on Neural Networks, Deep Learning, Probabilistic models, Probabilistic Learning, Unsupervised Learning, Reservoir Computing, Kernel-based methods, Support Vector Machines, Adaptive processing of structured information, Signal and Image processing, and other Pattern Recognition techniques.
The research is aimed at defining frameworks for the development of intelligent systems and intelligent data analysis tools for learning in structured domains (sequences, trees, graphs) and for the application to innovative interdisciplinary fields.

The application fields include Health and biomedical informatics, BioInformatics, ChemInformatics, Robotics, Intelligent/Adaptive Wireless Sensor Networks, and Signal/Image Processing, Ambient Assisted Living and Human Activity.
CIML group has participated in many EU and Italian funded projects.

Sectors :
ICT for Health / e-healthBioinformatics/Big data analysis
Artificial Intelligence based systems/DSS
Pharma/BiopharmaRelated activities and servicesArtificial Intelligence based solutions
Medical devices/Biotech (non pharma)Related activities and servicesArtificial Intelligence based solutions

Field of Research: 
AI, simulation and data processing
Image analysis & processing
Data analysis (big data, pattern recognition, data mining)
Information and computing sciences

Skills: 
Machine Learning Neural Networks for Structured Domains Neural Networks Deep Learning Reservoir Computing Generative Approaches Kernel-based methods Unsupervised Learning Data Mining
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Fondazione Toscana Life Sciences - Progetto co-finanziato nel quadro del POR FESR Toscana 2014-2020
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