Learning, Processing and Querying Data
Modern artificial intelligence benefits from two major advantages: data availability and computational power. Data is now available, often in large quantities and from multiple sources, and can be tainted by various imperfections (missing, inaccurate, heterogeneous data and so on.). Managing this massive and heterogeneous data raises several challenges for the AI community. From data mining to machine learning, several current problems require computationally efficient solutions that can provide usable, reliable and explainable results for the user.
Thus, the new “Data” research axis within CRIL has set as its main goals:
- The proposal of new algorithms for knowledge extraction and machine learning;
- The study and analysis of fundamental, algorithmic and experimental aspects of knowledge extraction and machine learning techniques;
- The proposal of efficient solutions for the management of massive, heterogeneous and complex data by integrating of confidentiality and reliability aspects;
- Cross-fertilization and exploitation of strong synergies with the two other thematic axes of CRIL (such as the development of symbolic and declarative approaches for data mining and explainability based on the strength of modern solvers and reasoners);
- The collection, completion and interrogation of massive and heterogeneous databases;
- Modeling and design of knowledge extraction and artificial learning pipelines in some application fields.
Keywords:
- Data mining and data science
-
knowledge extraction (pattern and rule extraction, clustering, communities,…), declarative approaches, data quality
- Machine learning
-
machine learning and explainability, reliability, calibration
- Data management
-
Query, completion, access control, confidentiality, repair
- Applications
-
Recommendation, anomaly detection, community detection,and so on.
Members in the axis
Audemard Gilles | Professor | iut |
Bellart Steve | PhD student | fac |
Benferhat Salem | Professor | fac |
Bounia Louenas | PhD student | fac |
Bouraoui Zied | Associate professor, accredited to supervise research | fac |
Cheikh-Alili Fahima | Associate professor | iut |
Condotta Jean-François | Professor | iut |
Delorme Fabien | Engineer | fac |
Falque Thibault | PhD student | fac |
Hidouri Amel | Associate member | fac |
Ing David | PhD student | fac |
Jabbour Saïd | Professor | fac |
Kebir Sara | PhD student | fac |
Kemgue Alain Trésor | Engineer | fac |
Koriche Frédéric | Professor | iut |
Lagniez Jean-Marie | Professor | fac |
Le Berre Daniel | Professor | fac |
Lecoutre Christophe | Professor | iut |
Lonca Emmanuel | Engineer | fac |
Marquis Pierre | Professor | fac |
Mazure Bertrand | Professor | fac |
Mengel Stefan | CNRS researcher, accredited to supervise research | fac |
Saïs Lakhdar | Professor | fac |
Salhi Yakoub | Professor | iut |
Tabary Sébastien | Associate professor | iut |
Tabia Karim | Associate professor, accredited to supervise research | fac |
Tahil Gökhan | PhD student | fac |
Tran Nguyen Duong Chi | PhD student | fac |
Wallon Romain | Associate professor | iut |
Ongoing PhDs
- Determination of the Association Constant between a Modified Cyclodextrin and a Guest by Artificial Intelligence for Valorization of Bio-Sourced Substrates by using Green Chemistry Principles - Gökhan Tahil
- Découverte de connaissances à partir de données migratoires - David Ing
- Formal models for explainable and robust AI - Louenas Bounia
- Knowledge Compilation for explainable and robust AI - Steve Bellart
- Learning Interpretable Circuits - Chi Tran Nguyen Duong
- Lightweight approaches for a Smart home solution for the elderly - Sara Kebir
- Optimization of passenger flows and resource management through machine learning and constraint programming techniques - Thibault Falque