My research sits around AI for science: building representations and models that help researchers navigate the space of possible scientific hypotheses.

During my PhD at Vrije Universiteit Amsterdam, I worked on knowledge graphs and transformers for hypothesis generation. A recurring theme in that work was representation: whether a hypothesis is written as text, encoded as a link, expressed as a graph, or represented through a mixture of symbolic and neural methods changes what a model can learn and what a researcher can inspect.

Current focus

  • AI and machine learning for Life Sciences.
  • Knowledge graphs for scientific discovery.
  • Query answering and link prediction over incomplete or richly structured graphs.
  • Evaluation of AI-generated scientific research and hypotheses.
  • Practical data science systems that make research evidence easier to use.

Background

I completed my PhD in the Knowledge Representation and Reasoning group at Vrije Universiteit Amsterdam, supervised by Frank van Harmelen and Michael Cochez. I was also part of Discovery Lab, a collaboration between VU, UvA, and Elsevier.

I now work as a Senior Data Scientist at Elsevier on Life Sciences, where the same themes continue in a more applied setting: connecting scientific content, structured knowledge, and machine learning into useful research-facing tools.

For the formal record, see Publications.