My name is Szymon Gustav Snoeck and I am a fourth-year applied mathematics major at Columbia University, minoring in computer science. My main research interests are in algorithms and machine learning for high-dimensional data. However, through guided research projects and graduate classes, I have also done research in nearest-neighbor search, learning theory, matching theory, dimension-reduction, and derandomization. Some of the work I have produced can be found below.

In the fall, I will be starting my PhD at the Computer Science Department of the Courant Institute of Mathematical Sciences at New York University. My research is supported by the NSF Graduate Research Fellowship.

• t-SNE Exaggerates Clusters, Provably.
  Noah Bergam, Szymon Snoeck, Nakul Verma.
  International Conference on Learning Representations (ICLR), 2026. arxiv
• Compressibility Barriers to Neighborhood-Preserving Data Visualizations.
  Szymon Snoeck, Noah Bergam, Nakul Verma.
  Algorithmic Learning Theory (ALT), 2026. arxiv

• A Uniform Convergence Result for Learning Text Data.
  Szymon Snoeck.
  Manuscript, 2025. pdf
• The Negative Inter-Dependencies of the Multivariate Hypergeometric Distribution.
  Szymon Snoeck.
  Manuscript, 2025. pdf
• The Difficulty of Approximating NSW in Online Matching.
  Szymon Snoeck, Christopher En, Yuri Faenza.
  Manuscript, 2024. pdf
• Deterministic Approximate Counting F2 Polynomials Via Correlation-based Fourier Bounds.
  Szymon Snoeck, Sam Wang.
  Manuscript, 2024. pdf