diff --git a/_bibliography/DavidWessels.bib b/_bibliography/DavidWessels.bib
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+++ b/_bibliography/DavidWessels.bib
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+@inproceedings{knigge2024equivariant,
+  title={Equivariant Neural Fields For Symmetry Preserving Continous PDE Forecasting},
+  author={Knigge, David M and Wessels, David and Valperga, Riccardo and Papa, Samuele and Gavves, Stratis and Bekkers, Erik J},
+  booktitle={ICLR 2024 Workshop on AI4DifferentialEquations In Science}
+}
+@inproceedings{ranum2024ngt200,
+  title={The NGT200 Dataset-Geometric Multi-View Isolated Sign Recognition},
+  author={Ranum, Oline and Wessels, David and Otterspeer, Gom{\`e}r and Bekkers, Erik J and Roelofsen, Floris and Andersen, Jari I},
+  booktitle={ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling},
+  year={2024}
+}
diff --git a/_people/DavidWessels.md b/_people/DavidWessels.md
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+---
+layout: person-page
+first_name: David
+middle_names: Robbert
+last_name: Wessels
+role: PhD candidate
+email: d.r.wessels@uva.nl
+lab: AMLab
+institute: Institute of Informatics
+university: University of Amsterdam
+supervisors: [Erik Bekkers, Efstatios Gavves]
+one_liner: Geometry grounded representations in deep learning
+redirect: 
+description: |
+    I am a PhD student at the [Amsterdam Machine Learning Lab](/) (AMLab) under the supervision of [Erik Bekkers](https://ebekkers.github.io/) and co-supervised by [Efstratios Gavves](https://www.egavves.com/) both located at the University of Amsterdam. My research focuses on the intersection of machine learning and geometry, with a particular emphasis on developing data representations grounded in their inherent geometric structures. I am especially interested in applications with tangible real-world impact, such as medical imaging and molecular generation.
+img: assets/img/DavidWessels.jpg
+img_promo: 
+personal_page:
+scholar_userid: pDL-0HoAAAAJ
+github_username: dafidofff
+twitter_username: dafidofff
+category: PhD Students 
+---
+
+{% include person_page.html person=page %}
\ No newline at end of file
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