Curriculum vitae
Applied machine learning for urban transport and city logistics.
I’m a PhD researcher in TU Delft’s Department of Transport & Planning, specialising in applied machine learning for urban transport and city logistics. My work connects forecasting and reinforcement learning with shared mobility, fleet operations and last-mile delivery.
Research and delivery
- Doctoral researcher, TU Delft Department of Transport & Planning, June 2023–present: probabilistic forecasting, graph-based multi-task learning and operational decision-making.
- SUM industry deployment: led modelling and coordinated delivery of availability predictions in RET’s live travel-planning app.
- T-STAR: transformer-based probabilistic demand forecasting, published in Transportation Research Part C in 2026.
- STAMP: joint demand and charge-aware supply forecasting, under review at Transportation Research Part C.
- Meal-delivery research: forecasting, constrained clustering and deep reinforcement learning in a collaboration with Takeaway.com.
Technical strengths
Python · PyTorch · Hugging Face models · OpenAI Gym · XGBoost · Probabilistic forecasting · Transformers · Graph learning · Deep reinforcement learning
Education
- PhD researcher, Delft University of Technology, June 2023–present.
- MSc Computational Science, University of Amsterdam, 2021–2023. Final grade 8.9/10.
- BSc Econometrics and Operations Research, Erasmus University Rotterdam, 2018–2021. Final grade 8.0/10.