RP

About

I'm a PhD candidate at KU Leuven, working on knowledge graphs and multimodal ML for cultural heritage.

Currently — building ArtKB and writing the next paper.

My work sits at the intersection of knowledge representation and machine learning — building systems that make heterogeneous data more findable, interoperable, and reusable.

Before the PhD, I spent two years as a research software engineer at VITO, working on data interoperability for environmental health studies. That experience shaped how I think about real-world data problems.

Education

  1. 2024 – present

    PhD Researcher

    KU Leuven

    Knowledge graphs and multimodal ML for cultural heritage

  2. 2020 – 2022

    MSc Industrial Engineering — Information Science

    Ghent University (UGent)

  3. 2016 – 2020

    BSc Industrial Engineering — Information Science

    Ghent University (UGent)

Background

I studied industrial engineering with a focus on information science at Ghent University, where I first got interested in how structured data can bridge different systems and domains.

After graduating, I joined VITO as a research software engineer, building tools for data harmonisation in human biomonitoring studies across European research consortia. That work taught me that the gap between data and usable knowledge is where the hardest and most impactful problems live.

In May 2024, I started a PhD at KU Leuven to focus on that gap full-time — specifically at the intersection of knowledge graphs, multimodal machine learning, and cultural heritage.

What drives the work

I believe that structured, open knowledge is the prerequisite for AI that is genuinely useful in complex domains. Models need context, not just data — and knowledge graphs are how you give them that context. My goal is to build systems where domain expertise and machine intelligence reinforce each other.

Outside the lab

  • Chess — mostly online, occasionally over the board
  • Sports — both playing and overanalysing the data
  • Gaming — from strategy to story-driven

Tools & technologies

Languages
Python, TypeScript, Java, SPARQL
Semantic Web
RDF, OWL, SHACL, Linked Data, Comunica
ML & Data
PyTorch, Hugging Face, pandas, Jupyter
Web
Astro, D3.js, Node.js