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.

News:

Spring 2026: I am happy to share that I was selected as a finalist for the CRA Outstanding Undergraduate Researcher Award, and my two papers have been accepted at ALT 2026 and ICLR 2026.

Spring 2026: I have been selected as the winner of the 2026 Undergraduate Applied Mathematics Faculty Award, which is awarded to one graduating applied mathematics student each year for their academic achievements during their time at Columbia University.

• 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