The DIG team is part of Télécom Paris, a member of Institut Polytechnique de Paris, France. We work on knowledge graphs, language models, foundational models, learning over tabular data, graph mining, and stream mining. The team develops methods for representing, integrating, and reasoning over complex, dynamic data to enable interpretable and trustworthy AI. Applications range from general-purpose AI to domain-specific areas such as healthcare and law.
News
Feb 18, 2026: DIG has five articles accepted at ICLR 2026
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TabStruct: Measuring Structural Fidelity of Tabular Data. Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik
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Query-Level Uncertainty in Large Language Models. Lihu Chen, Fabian M. Suchanek, Gaël Varoquaux, Gerard de Melo
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Efficient Resource Constrained Training of Vision Transformers via Subspace Optimization. Le-Trung Nguyen, Enzo Tartaglione, Van-Tam Nguyen
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Study of Training Dynamics for Memory-Constrained Fine-tuning. Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi, Van-Tam Nguyen, Enzo Tartaglione
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INSTANT: Compressing Gradients and Activations for Resource-Efficient Training. Tuan-Kiet Doan, Trung-Hieu Tran, Enzo Tartaglione, Nikola Simidjievski, Van-Tam Nguyen
Dec 01, 2025: Yanzhu Guo joined DIG
We’re happy that Yanzhu Guo joined us as an assistant professor in the team! Welcome, Yanzhu!
Nov 01, 2025: Best Paper Award at ISWC 2025
Yiwen Peng, Thomas Bonald and Fabian Suchanek received the Best Paper Award at ISWC 2025 for their paper “FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic.”