PeopleofSuperintelligence

Researcher

Daniel Daza

Also known as Daniel Fernando Daza Cruz, Daniel Daza Cruz

Daniel Daza is a machine learning researcher whose work develops ways for AI systems to reason over knowledge graphs and use graph learning in science. He coauthored Message Passing Query Embedding, a method for complex graph queries; his 2021 work on neural link predictors won an ICLR Outstanding Paper Award, and BioBLP extended graph embeddings across biomedical data modalities. He completed a cum laude PhD at Vrije Universiteit Amsterdam in 2024 and coauthored GRAPES, an adaptive graph-sampling method published in 2025. As of October 2026, he is a postdoctoral researcher at Eindhoven University of Technology, developing knowledge graphs and multimodal foundation models for materials discovery through SimuLingua.

Timeline

  1. 2020

    Message Passing Query Embedding

    In 2020, Daza and Michael Cochez introduce Message Passing Query Embedding, a method that uses a graph neural network to encode query graphs whose nodes represent entities and variables.

  2. 2021

    An ICLR Outstanding Paper

    In 2021, he coauthors Complex Query Answering with Neural Link Predictors with Erik Arakelyan, Pasquale Minervini, and Michael Cochez. It reuses one-hop link predictors to answer larger knowledge graph queries and receives an ICLR Outstanding Paper Award. He also coauthors Inductive Entity Representations from Text via Link Prediction, which evaluates pretrained language models on entities unseen during training.

  3. 2023

    Biomedical knowledge graph embeddings

    With five coauthors, he publishes BioBLP in 2023, a framework for knowledge graph embeddings that combines entity attributes across modalities and supports missing attributes. In October 2023, he and Michael Cochez give a CIKM tutorial on knowledge graph embeddings and reasoning beyond triples.

  4. July 2024

    Postdoctoral work at Amsterdam UMC

    In July 2024, he starts a postdoc at Amsterdam UMC, applying machine learning on graphs to healthcare. His work there includes knowledge graphs and rare-disease research.

  5. December 2024

    A PhD on knowledge graphs

    In December 2024, he completes a PhD at Vrije Universiteit Amsterdam cum laude, with a dissertation titled Exploiting Subgraphs and Attributes for Representation Learning on Knowledge Graphs.

  6. 2025

    Sampling graphs with GRAPES

    In 2025, he coauthors GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. The method learns adaptive node-sampling probabilities to choose the nodes that matter for training a graph neural network.

  7. 2026

    An ICML Gold Reviewer

    In 2026, he is recognized as an ICML Gold Reviewer.

  8. As of October 2026

    Postdoc at Eindhoven

    As of October 2026, he is a postdoctoral researcher in Eindhoven University of Technology's High-Performance Scientific Computing group, working on knowledge graphs and multimodal foundation models for materials discovery through SimuLingua. Before joining TU/e, he works as a postdoc in VU Amsterdam's Learning & Reasoning Group.

Career

  1. NowEindhoven University of TechnologyPostdoctoral Researcher
  2. From 2024Amsterdam UMCPostdoctoral Researcher
  3. Vrije Universiteit AmsterdamPostdoctoral Researcher

Education

  1. 2024Vrije Universiteit AmsterdamPhD

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Daniel Daza. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Eindhoven University of Technology. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Eindhoven University of Technology. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Message Passing Query Embedding arxiv.org. Checked 22 October 2026.
  2. Outstanding paper award at ICLR 2021: ‘Complex Query Answering with Neural Link Predictors’ | Discovery Lab discoverylab.ai. Checked 22 October 2026.
  3. Inductive Entity Representations from Text via Link Prediction arxiv.org. Checked 22 October 2026.
  4. BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs arxiv.org. Checked 22 October 2026.
  5. L&R | Learning and reasoning lr.cs.vu.nl. Checked 22 October 2026.
  6. Daniel Daza dfdazac.github.io. Checked 22 October 2026.
  7. Exploiting Subgraphs and Attributes for Representation Learning on Knowledge Graphs - Vrije Universiteit Amsterdam research.vu.nl. Checked 22 October 2026.
  8. GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks - Vrije Universiteit Amsterdam research.vu.nl. Checked 22 October 2026.
  9. Daniel Daza Cruz - Research portal Eindhoven University of Technology research.tue.nl. Checked 22 October 2026.