Daniel Daza
Postdoctoral Researcher
Learning & Reasoning Group
Vrije Universiteit Amsterdam
I study artificial intelligence methods for representing and learning from structured scientific data, especially knowledge graphs. My work spans graph representation learning, complex query answering, foundation models for knowledge graphs, and applications in scientific discovery and healthcare.
I completed my PhD at Vrije Universiteit Amsterdam cum laude in 2024. Before joining VU Amsterdam as a postdoctoral researcher in January 2026, I worked as a postdoctoral researcher at Amsterdam UMC on machine learning for knowledge graphs and rare disease research.
Recent Highlights
- Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints has been accepted in Transactions on Machine Learning Research, extending query answering with constraints that are difficult to express in first-order logic.
- Explaining Graph Neural Networks for Node Similarity on Graphs has been accepted in Transactions on Machine Learning Research, studying how to explain learned notions of similarity in graph neural networks.
- Our project Large Laboratory Models received computing time on national compute facilities via the NWO Large Compute Application program, with 300k CPU and 2M GPU compute credits.
Selected Publications
For a complete publication list, see Google Scholar.
*Indicates equal contribution.
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Masoud Mansoury, Michael Cochez, Martijn Schut.
Explaining Graph Neural Networks for Node Similarity on Graphs
Daniel Daza, Cuong Xuan Chu, Trung-Kien Tran, Daria Stepanova, Michael Cochez, Paul Groth.
Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation
Yannick Brunink*, Daniel Daza*, Yunjie He, Michael Cochez.
Contextualized Interpretation of Machine-Learning Predictions in Rare Disease Omics
Daniel Daza, Yorrick Jaspers, Alberto Bernardi, Luca Costabello, Christophe Gueret, Michael Cochez, Marc Engelen, Stephan Kemp, Martijn Schut.
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam, Peter Bloem.
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.
Complex Query Answering with Neural Link Predictors
Erik Arakelyan*, Daniel Daza*, Pasquale Minervini*, Michael Cochez.
Last updated: May 27, 2026.