PeopleofSuperintelligence

Researcher

Nicholas Sharp

Also known as nmwsharp

Nicholas Sharp is a researcher in geometry processing, computer graphics and 3D machine learning whose work seeks new algorithms and representations that make computing with geometric data easy, efficient and reliable. He was an undergraduate at Virginia Tech, defended his Carnegie Mellon PhD thesis under Keenan Crane in 2021, and did a postdoc with Alec Jacobson at the University of Toronto. Spelunking the Deep, written with Jacobson, won a Best Paper Award at SIGGRAPH 2022, and The Affine Heat Method, written with Yousuf Soliman, won the Best Paper Award at SGP 2025. He joined NVIDIA as a senior research scientist in August 2022, where he coauthored Adaptive Shells, a SIGGRAPH Asia 2023 Best Paper, and PartField, a feedforward model that splits 3D shapes into parts. He is a senior research scientist at NVIDIA in Seattle and joins the University of Washington's Allen School as an associate professor in January 2027.

Now at
NVIDIA
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Timeline

  1. By 2016

    Systems biology at Virginia Tech

    As an undergraduate at Virginia Tech, he works with T.M. Murali on computational systems biology. He coauthors Xtalk, a path-based approach for identifying crosstalk between signaling pathways, published in Bioinformatics in 2016.

  2. 2018–2021

    A PhD under Keenan Crane

    He pursues a PhD in computer science at Carnegie Mellon University, advised by Keenan Crane, and presents Variational Surface Cutting at SIGGRAPH 2018 and Navigating Intrinsic Triangulations at SIGGRAPH 2019. In 2020, their paper A Laplacian for Nonmanifold Triangle Meshes receives the Best Student Paper Award at SGP, and he coauthors PointTriNet with Maks Ovsjanikov, a neural method that triangulates 3D point sets. He defends his thesis on intrinsic triangulations in May 2021.

  3. 2021–2022

    Postdoc with Alec Jacobson

    He starts a postdoc with Alec Jacobson in the University of Toronto's DGP in August 2021. In 2022, he coauthors VectorAdam with Selena Ling and Jacobson at NeurIPS, a change to the Adam optimizer that makes it rotation-equivariant for vector-valued parameters.

  4. 2022

    A SIGGRAPH 2022 best paper

    With Alec Jacobson, he receives a Best Paper Award at SIGGRAPH 2022 for Spelunking the Deep, which evaluates geometric queries such as ray casting and closest-point on neural implicit surfaces. He coauthors DiffusionNet with Souhaib Attaiki, Keenan Crane and Maks Ovsjanikov, a deep-learning method for triangle meshes and point clouds, presented at SIGGRAPH 2022. His Polyscope viewer for geometric algorithms receives the Software Award at SGP 2022.

  5. 2022–2023

    NVIDIA and Adaptive Shells

    He starts a full-time role as a Senior Research Scientist at NVIDIA in August 2022, based out of Seattle. In 2023, he is a coauthor of Adaptive Shells for Efficient Neural Radiance Field Rendering, which receives a Best Paper Award at SIGGRAPH Asia 2023 and speeds up neural field rendering by extracting bounding shell meshes.

  6. 2024–2025

    SpaceMesh, PartField and Affine Heat

    In 2024, he coauthors SpaceMesh, a neural representation that generates manifold polygonal meshes directly through a halfedge mesh structure. In 2025, he is a coauthor of PartField, a feedforward model that learns 3D feature fields for part segmentation and appears at ICCV 2025. The Affine Heat Method, written with Yousuf Soliman, receives the Best Paper Award at SGP 2025.

  7. As of October 2026

    Joining the UW Allen School

    He is a Senior Research Scientist at NVIDIA, based out of Seattle. In January 2027, he joins the Paul G. Allen School of Computer Science & Engineering at the University of Washington as an Associate Professor (pre-tenure). His research in geometry processing, computer graphics/vision and 3D machine learning seeks new algorithms and representations that make computing with geometric data easy, efficient and reliable.

Career

  1. Since 2022NVIDIASenior Research Scientist
  2. 2021–2022University of TorontoPostdoc

Education

  1. 2021Carnegie Mellon UniversityPhD
  2. Virginia Tech

From the biography.

  1. 16% of AI career moves in the 2020s went from a university to a companyIncludes Nicholas Sharp. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.
  2. Google has the highest combined h-index among AI organizationsIncludes NVIDIA. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes NVIDIA. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes NVIDIA. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

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

Sources and credits

Photo: We have no published photo of Nicholas Sharp yet. Send us a free-licensed photo.

  1. Nicholas Sharp nmwsharp.com. Checked 22 October 2026.
  2. Nicholas Sharp – LOGML 2026 logml.ai. Checked 22 October 2026.
  3. [2005.02138] PointTriNet: Learned Triangulation of 3D Point Sets arxiv.org. Checked 22 October 2026.
  4. [2205.13599] VectorAdam for Rotation Equivariant Geometry Optimization arxiv.org. Checked 22 October 2026.
  5. Technical Papers | SIGGRAPH 2022 s2022.siggraph.org. Checked 22 October 2026.
  6. [2012.00888] DiffusionNet: Discretization Agnostic Learning on Surfaces arxiv.org. Checked 22 October 2026.
  7. Department of Computer Science at SIGGRAPH Asia 2023 web.cs.toronto.edu. Checked 22 October 2026.
  8. [2409.20562] SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes arxiv.org. Checked 22 October 2026.
  9. [2504.11451] PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond arxiv.org. Checked 22 October 2026.
  10. BCAM hosts successful edition of the Symposium on Geometry Processing 2025 | BCAM - Basque Center for Applied Mathematics bcamath.org. Checked 22 October 2026.
  11. Nicholas Sharp, Associate Professor (Arriving January 2027) - Paul G. Allen School of Computer Science & Engineering cs.washington.edu. Checked 22 October 2026.