Celia Rubio‑Madrigal


About me

I am a PhD candidate at CISPA Helmholtz Center for Information Security in Saarland, Germany, supervised by Dr. Rebekka Burkholz at the Relational ML Lab.

I study ways to improve the generalization of graph neural networks (GNNs), with a focus on how graph structure and depth affect their behavior in theory and practice. I have worked on directions such as rewiring, tabularization, and knowledge distillation, with an emphasis on empirical evaluation and testing the limits of common assumptions. More broadly, I am interested in developing architectures that balance the trade-offs between expressiveness, efficiency, and generalization; in this regard, I have collaborated on broader topics such as tabular model robustness and optimization. My work has been published at NeurIPS, ICLR, and ICML [1, 2, 3] and presented in several talks and workshops.

My CV is available here.

Background

I obtained two undergraduate degrees in Mathematics and Computer Science from Universidad Complutense de Madrid. Afterwards, I was awarded a postgraduate fellowship from la Caixa Foundation, which fully supported my master’s studies at the University of Strathclyde in the UK, where I was awarded the Departmental Best Overall Performance Prize.

Since 2023, I annually attend the European Girls’ Mathematical Olympiad (EGMO) as IT support with Joseph Myers. I have been selected twice for the Heidelberg Laureate Forum: in 2022 and in 2026. I have also developed two music notation packages for LaTeX: mousik (2026) and ddphonism (2019).

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