I am a postdoctoral researcher at the Translational AI Laboratory in the Amsterdam University Medical Center. I do research on machine learning methods that helps us structure and exploit our knowledge about the world, with applications to search and scientific discovery. This ranges from methods that help us build structured representations, such as methods for information extraction and knowledge graph construction from text, to those that exploit structured representations, such as algorithms for graph representation learning, link prediction, and complex query answering over incomplete KGs.

Recent Highlights

• GRAPES 🍇 has been accepted in Transactions on Machine Learning Research! we propose a method for scaling GNNs to graphs with up to 2M nodes and 60M edges.
• I have defended my PhD with distinction cum laude. My thesis is avalable at this link.
• UnRavL has been accepted to the 2024 Learning on Graphs conference!
• Our paper, Adapting Neural Link Predictors for Data-Efficient Complex Query Answering has been accepted to NeurIPS 2023!

Selected Publications

For a full list of publications, see Google Scholar.

GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam, Peter Bloem, in Transactions on Machine Learning Research (2025).
[paper] [code]

UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
Tamara Cucumides, Daniel Daza, Pablo Barcelo, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero Orth, in Learning on Graphs Conference 2024.
[paper]

Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, and Isabelle Augenstein, in NeurIPS 2024.
[arxiv] [code]

BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, and Paul Groth, in Journal of Biomedical Semantics (2023).
[paper] [code]

SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning
Daniel Daza, Michael Cochez, and Paul Groth, in ACL 2022 Workshop on Structured Prediction for NLP.
[paper] [code] [bibtex]

Inductive Entity Representations from Text via Link Prediction
Daniel Daza, Michael Cochez, and Paul Groth, in The Web Conference 2021.
[arXiv] [code] [bibtex]

Last update: May 29 2025.