The LINCS is strongly involved in research related to AI, from the most theoretical aspects (learning theory, sample complexity, information-theoretic bounds) to the development of open-source software (see the Python package scikit-network for graph analysis) and the application to real use cases (cyber-security, anomaly detection, automatic classification of technical documents, content recommandation, text style transfer). We are interested in any type of data, but more especially in knowledge graphs, databases, texts, logs and time-series. Our research aims at making AI efficient, robust and explainable.
Data Mining
- Clustering
- Embedding/Representation learning
- Hierarchical clustering
- Metric learning
- Knowledge graphs
- Time series analysis
- Causality inference
Foundations of Machine Learning
- Possibilities and limitations of machine learning
- Information theory and machine learning
- Active learning
- Transfer learning
- Online learning
- Decentralized learning
- Robustness and security of learning algorithms
Applications of AI
- Traffic and performance prediction
- Network design
- Anomaly detection
- Alarm logs
- Localization
- Social networks
- Content recommendation
- Personalized medicine
- NLP
- Predictive maintenance
- Smart grids
Some LINCS members active on these topics
| Photo | Fullname | Institution | Office | Homepage |
|---|---|---|---|---|
 |
Thomas Bonald | ■ Institut Mines-Telecom | 51 |  |
 |
Anne Bouillard | ■ Nokia Bell Labs | 31 | |
 |
Elie de Panafieu | ■ Nokia Bell Labs | 42 | |
 | François Durand | ■ Nokia Bell Labs | 42 | |
 | Philippe Jacquet | ■ Nokia Bell Labs | 42 | |
 | Leonardo Linguaglossa | ■ Institut Mines-Telecom | 23 | |
 | Laurent Massoulié | ■ Inria | 30 | |
 | Dimitrios Milioris | ■ Nokia Bell Labs | 36 | |
 | Alexandre Proutière | ■ Inria | 30 | |
 | Mauro Sozio | ■ Institut Mines-Telecom | |