Teaching (spring 2025)

• Introduction to Topological Data Analysis (course website). 

News

• (Jan) My preprint Computational complexity of sum-of-squares bounds for copositive programs
  with Marilena Palomba, Luis Felipe Vargas, and Monaldo Mastrolilli is on arXiv

• (Dec) An expository article on my thesis research was published in Nieuw Archief voor Wiskunde.

• (Sep) My paper Testably Learning Polynomial Threshold Functions with Stefan Tiegel and Manuel Wiedmer was accepted to NeurIPS 2024.

• (Sep) My preprint A sparsified Christoffel function for high-dimensional inference with Jean-Bernard Lasserre is on arXiv.

About me

From November 2022, I am a postdoc in the group of David Steurer at ETH Zürich.

Previously, I was a PhD student at Centrum Wiskunde & Informatica (CWI) in the Networks & Optimization group, under the supervision of Monique Laurent.
I defended my thesis cum laude at Tilburg University on 30 September 2022.

You can view my CV here (updated April 2025).

Scientific awards

• My thesis won the Stieltjes prize for best thesis in mathematics defended at a Dutch university in the academic year 2022-2023.

• My paper Sum-of-squares hierarchies for polynomial optimization and the Christoffel-Darboux kernel won the SIAM student paper prize 2024.

• I won the KWG PhD Prize 2022 of the Royal Dutch Mathematical Society.

• My thesis received an honourable mention for the Christiaan Huygens Science Award 2025.

Research interests

My main interest is in polynomial optimization and semidefinite programming approaches to problems in optimization, discrete geometry, combinatorics, statistics and data science. Recently, I am also interested in (algorithmic) learning theory and topological data analysis.

Some topics I have worked on are:

• Asymptotic error analysis of various sum-of-squares hierarchies.

• Bounds on the bit-complexity of SoS-proofs.

• Extensions of the Lovász theta-number to (geometric) hypergraphs.

• Applications of reproducing (Christoffel-Darboux) kernels to (topological) data analysis and statistical inference.

• Methods based on polynomial regression for algorithmic learning theory.