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TRESP Lab @ LMU Munich
We advance representation learning for knowledge graphs, multimodal learning, and AI-driven understanding—building systems that integrate text, images, and video into structured, actionable world models. oai_citation:0‡TRESP Lab
🔭 Research Directions
- Temporal & hyper-relational knowledge graphs; inductive reasoning and link prediction. oai_citation:1‡MCML
- Multimodal foundation models for cognitive AI and human-level understanding. oai_citation:2‡TRESP Lab
- Robust adaptation of vision-language models and open dynamic graph benchmarks. oai_citation:3‡NeurIPS
👥 People
Lead: Prof. Dr. Volker Tresp (LMU Munich).
🏛️ Affiliation
Database Systems, Data Mining and AI, LMU Munich; activities within MCML and broader Munich AI ecosystem. oai_citation:5‡ifi.lmu.de
📚 Selected Activities & Topics
- Memory embeddings, tensor models, and the Tensor Brain line of work. oai_citation:6‡Qcssc
- Courses & seminars on Generative AI and machine learning at LMU. oai_citation:7‡ifi.lmu.de
🤝 Collaborate with Us
We welcome collaborations on:
- Multimodal/streaming understanding with structured memory
- Temporal KGs and trustworthy reasoning for real-world data
- Domain adaptation of large VLMs and dynamic graph evaluation
(See site for people, publications, and openings.) oai_citation:8‡TRESP Lab
🔗 Useful Links
- 🌐 Website: tresp-lab.github.io oai_citation:9‡TRESP Lab
- 👥 People: /people oai_citation:10‡TRESP Lab
- 🧪 Group @ MCML: mcml.ai/research/groups/tresp oai_citation:11‡MCML
- 📇 Prof. Tresp: dbs.ifi.lmu.de/~tresp oai_citation:12‡ifi.lmu.de
“We push the limits of AI by developing structured, interpretable models of the world.” oai_citation:13‡TRESP Lab