I am a first-year PhD student in the Courant Institute Computer Science Department at New York University. I am advised by Gautam Kamath and Anupam Gupta. My research interests are in high-dimensional algorithms and the theory of machine learning. I have also worked on dimension reduction, learning theory, matching theory, and derandomization.

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 Nash Social Welfare 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