Fields of interest

The team leverages about 4 permanent researchers whose computer skills and research interests are complementary. They are brought together by the aim to produce added value from data (I.e., models, useful patterns or knowledge).

More specifically, the team is working on the following problems:

• Speaker Recognition
• Language Recognition
• Speaker Diarization
• Emotion Recognition
• Supervised/unsupervised learning with and without constraints
• Recommender systems
• Subgroup Discovery / Exceptional Model Mining
• User group Analytics
• Output Space Sampling
• Black Box model introspection

Our results are theoretical, methodological, algorithmic, software, and applications. Our guiding principle is to try to help data owners throughout the interactive process of knowledge discovery from data. Such processes require the combination of a wide range of paradigms of description or induction (pattern extraction, classification, statistical learning, deep learning, etc.). Applications

Our research is developed in relation to real data analysis: the quantitative and qualitative empirical study on real data is fundamental to assessing the performance of the proposed methods. While AI research team develops methods and algorithms rather than applications, it works with owners of data from several environments: Neuroscience, Chemistry, Social Sciences, Industry, etc. This diversity shows our willingness to be centered “Methods” and develop generic algorithms applicable to a broad spectrum of applications.

Applications

Our research is developed in relation to real data analysis: the quantitative and qualitative empirical study on real data is fundamental to assessing the performance of the proposed methods. While AI research team develops methods and algorithms rather than applications, it works with owners of data from several environments: Neuroscience, Chemistry, Social Sciences, Industry, etc. This diversity shows our willingness to be centered “Methods” and develop generic algorithms applicable to a broad spectrum of applications.

Contributions

The major annual conferences are: ECMLPKDD, SIGKDD, IEEE ICDM, SIAM DM, PAKDD, DSAA, IJCAI, AAAI, Neurips, ICML, ICRL,WWW, RecSys, WSDM, VLDB, EDBT, ICASSP, INTERSPEECH, Odyssey, SLT.

The major journals are: Journal of Machine Learning Research, Articificial Intelligence Journal, Pattern Recognition, Machine Learning, Neurocomputing, Data Mining and Knowledge Discovery, KAIS, IEEE TASLP, and Speech Communication.

Significant contributions can also occur in conferences (and journals) of data management (e.g., VLDB, ACM CIKM, IEEE TKDE, Information Systems).