DYNI : DYNamiques de l’Information

Keys Words

Artificial Intelligence, Deep Learning, Cognition, Bioacoustics, Speech, Hearing, Scene Understanding, Environmental Survey, Astrophysics

 

Head

Ricard MARXER

 

Permanent Members

GLOTIN HervéProfesseur des Universités
Courriel : herve.glotin@lis-lab.fr
MARXER RicardMaitre de Conférences
Courriel : ricard.marxer@lis-lab.fr
PAIEMENT AdelineMaitre de Conférences
Courriel : adeline.paiement@lis-lab.fr
PARIS SébastienMaitre de Conférences
Courriel : sebastien.paris@lis-lab.fr
RAZIK JosephMaitre de Conférences
Courriel : joseph.razik@lis-lab.fr

 

Phd Studients

BEST PaulDoctorant
Courriel : paul.best@lis-lab.fr
FERRARI MaxenceDoctorant
Courriel : maxence.ferrari@lis-lab.fr
MORGAN JayDoctorant
Courriel : jay.morgan@lis-lab.fr
PATRIS JulieDoctorant
Courriel : julie.patris@lis-lab.fr
POUPARD MarionDoctorant
Courriel : marion.poupard@lis-lab.fr

 

Other staff members

GIRAUDET PascaleCherch. associe
Courriel : pascale.giraudet@lis-lab.fr
MALIGE FranckPRAG
Courriel : franck.malige@lis-lab.fr

 

Research interests

Our aim is develop and innovate in methods of machine learning, signal processing and data analysis in order to improve our knowledge and understanding in physical, natural and human sciences.

DYNI’s research in Artificial Intelligence and representation learning aims to cover the data acquisition, transmission and processing chain from sensors to users. The research is applied to diverse fields such as, marine and maritime robotics, bioacoustics, speech & hearing, and multimodal information analysis in physics, health or cognition. Through its technological platform SMIoT, DYNI innovates the scientific instrumentation for smart long-term data acquisition and embedded data processing.

DYNI tackles the main challenges of data-driven approaches applied to the experimental sciences: a) high cost of data acquisition, b) human bias in manual annotations, c) complexity of the underlying phenomena, and d) explicatory power of advanced machine learning models. Thus, our work focuses on unsupervised/semi-supervised learning, reinforcement learning, Deep Learning and explainable AI applied to a variety of topics ranging from environmental monitoring to the study of vocal interactivity.

Prestigious researchers are invited to DYNI and its interaction with the economic, social and cultural environment is renowned. Its activity is very intense and diversified in the field of development (software, hardware, platform). It’s pioneering research activity has led to the creation of some of the first international congresses on environmental Big Data, the coordination of the Scaled Acoustic BIODiversity project (SABIOD), the EADM action of GDR MADICS, the organisation of ICLR 2017 and the co-organisation of the VIHAR community and workshops.



 

Site Web

More detail : https://dyni.lis-lab.fr

 

Scientific publications



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