PeopleofSuperintelligence

Researcher

Leland McInnes

Also known as L. McInnes

Leland McInnes is a mathematician and data scientist whose machine-learning work includes UMAP for dimension reduction and HDBSCAN clustering. He studied pure mathematics after starting in physics, worked at a casino analytics company in 2000, and later applied topology to unsupervised learning at the Tutte Institute. With John Healy and James Melville, he developed UMAP as a scalable method grounded in Riemannian geometry and algebraic topology, and he and Healy refined DBSCAN into HDBSCAN. As of October 2026, he is a research mathematician and data scientist at the Tutte Institute.

Now at
Tutte Institute for Mathematics and Computing

Timeline

  1. 2000

    Mathematics and casino analytics

    He starts in physics before switching to pure mathematics and earns a master's in algorithms in algebraic geometry. In 2000, he works at a company specializing in casino analytics and explores data visualization with self-organizing maps.

  2. February 2018

    UMAP for dimension reduction

    In February 2018, he posts the UMAP paper with John Healy and James Melville. Their scalable dimension-reduction technique is grounded in Riemannian geometry and algebraic topology and can serve as a general-purpose method for machine learning.

  3. July 2018

    Research at the Tutte Institute

    In July 2018, he is a research mathematician at the Tutte Institute and applies topological techniques to unsupervised learning. He holds a Ph.D. in profinite Lie rings.

  4. By August 2018

    Refining DBSCAN with HDBSCAN

    By August 2018, he and John Healy refine DBSCAN into HDBSCAN. Their high-performance Python implementation makes the algorithm orders of magnitude more efficient and becomes its de facto reference implementation.

  5. As of October 2026

    Research mathematician at Tutte

    As of October 2026, he is a research mathematician and data scientist at the Tutte Institute for Mathematics and Computing. He gives a keynote talk on high-dimensional data at the Workshop on Visualization for AI Explainability.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  3. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionWe estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Smuggled Gold and Untrustworthy Intuition — with Leland McInnes :: Posit Open Source opensource.posit.co. Checked 21 October 2026.
  2. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction arxiv.org. Checked 21 October 2026.
  3. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction speakerdeck.com. Checked 21 October 2026.
  4. Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) cse-cst.gc.ca. Checked 21 October 2026.
  5. 9th Workshop on Visualization for AI Explainability visxai.io. Checked 21 October 2026.