Research

Significant challenges in artificial intelligence and data management arise from the inherent characteristics of data. Data can vary widely in structure, size, dimensionality, and quality—it may be semi-structured, large, high-dimensional, incomplete, or noisy. On the other hand, data can also follow well-defined logical rules, with inherent patterns and constraints that can be leveraged to improve processing, inference, and decision-making. In the Tyrex project-team, we advance research at the interface of data management and artificial intelligence.

Projects

GraphRec (2024-..)

Through the ANR-funded GraphRec project, we investigate recursive navigational graph query processing, relational learning, and neuro-symbolic approaches to querying large graphs.

CLEAR (2017-2022)

Through the ANR-funded CLEAR project, we explored programming techniques to enhance the extraction of value from large-scale data. Our work included developing efficient methods for querying large graphs, generating optimized distributed code (e.g., for Spark) to process massive datasets, and designing scalable predictive models.