NVIDIA/torch-harmonics
Dec 2024 – Jul 2025
5 Months active
Languages Used
C++CUDAJinjaJupyter NotebookMarkdownPythonShellTOML
Technical Skills
C++CUDAChangelog ManagementCode RefactoringComputer VisionData Visualization
Boris Bonev
PROFILE
Boris Bonev
Over five months, Boris Bonev contributed to NVIDIA/torch-harmonics by developing advanced features for 3D scene understanding and spherical signal processing. He engineered spherical attention mechanisms, expanded 3D dataset support, and refactored core training workflows to improve SWE prediction and spectral analysis. His work included integrating Zernike and Morlet filter bases, enhancing distributed convolution normalization, and optimizing loss functions for spherical segmentation. Boris used Python, C++, and CUDA to implement robust numerical methods, improve code maintainability, and strengthen CI/CD pipelines. His efforts resulted in more flexible experimentation, improved model accuracy, and a more maintainable, well-documented codebase for research and production.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
42Total
Bugs
5
Commits
42
Features
12
Lines of code
18,663
Activity Months5
Your Network
1351 people
Work History
July 2025
7 Commits • 2 Features
Jul 1, 2025July 2025 — NVIDIA/torch-harmonics: Delivered codebase maintenance and spherical loss improvements, driving maintainability, stability, and model tooling reliability. Key work includes: (1) Codebase maintenance and documentation cleanup: removed unused get_psi, cleaned psi_idx docs, updated docstrings, adjusted weight decay handling, and updated the changelog; (2) Spherical loss function improvements: refactor for clearer docs, optimized gradients for normals, and improved L1/normal consistency in spherical segmentation; (3) Stability fixes addressing merge-related issues and an unintended sfno change; (4) Documentation and changelog synchronization to support faster onboarding and reproducibility. Overall impact: reduced technical debt, smoother contributor experience, and more predictable training/segmentation results.
7 Commits • 2 Features
Jul 1, 2025July 2025 — NVIDIA/torch-harmonics: Delivered codebase maintenance and spherical loss improvements, driving maintainability, stability, and model tooling reliability. Key work includes: (1) Codebase maintenance and documentation cleanup: removed unused get_psi, cleaned psi_idx docs, updated docstrings, adjusted weight decay handling, and updated the changelog; (2) Spherical loss function improvements: refactor for clearer docs, optimized gradients for normals, and improved L1/normal consistency in spherical segmentation; (3) Stability fixes addressing merge-related issues and an unintended sfno change; (4) Documentation and changelog synchronization to support faster onboarding and reproducibility. Overall impact: reduced technical debt, smoother contributor experience, and more predictable training/segmentation results.
July 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NVIDIA/torch-harmonics focusing on delivering customer value through 3D data capabilities, robust attention mechanisms, and improved development hygiene. The month emphasized shipping tangible features, stabilizing core math, and strengthening the docs/CI foundation to accelerate onboarding and reduce production risk.
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NVIDIA/torch-harmonics focusing on delivering customer value through 3D data capabilities, robust attention mechanisms, and improved development hygiene. The month emphasized shipping tangible features, stabilizing core math, and strengthening the docs/CI foundation to accelerate onboarding and reduce production risk.
April 2025
1 Commits
Apr 1, 2025Monthly summary for 2025-04 for NVIDIA/torch-harmonics focusing on bug fixes and stability of IRSHT transforms. Highlights include reverting zeroing of the imaginary parts in IRSHT, restoring standard FFT behavior and correctness.
1 Commits
Apr 1, 2025Monthly summary for 2025-04 for NVIDIA/torch-harmonics focusing on bug fixes and stability of IRSHT transforms. Highlights include reverting zeroing of the imaginary parts in IRSHT, restoring standard FFT behavior and correctness.
April 2025
January 2025
14 Commits • 5 Features
Jan 1, 2025January 2025 — NVIDIA/torch-harmonics monthly summary Key features delivered and notable fixes (business value in parentheses): - LSNO: Zernike Basis, Upsampling Options, and API Cleanup (new Zernike filter basis; multiple upsampling methods; cleaned parameter ordering; deprecated rank option removed). This expands modeling capabilities and simplifies usage for downstream pipelines. Commits: b6b2bce378df4f451985ab544cf4e7cc82bb941c; 3350099a70011bf0acb3dc96d056eee19ef9f0e7; 7126fb9a1af85e7ffb4f544fb3853917a6111255. - Morlet Filter Basis Enhancement (efficiency and accuracy): switch to Hann window for Morlet basis; updated defaults and related code. Commit: 780fd1435844b5ce672bfc40d0a83576e9e62824. - Psi Tensor Precision and Theta_cutoff Adjustment (accuracy and aliasing mitigation): increase psi tensor precision to double; add fudge factor for theta_cutoff to mitigate aliasing near poles. Commit: 15d0750cfc6a9c651a4020b942436c3613b5c90d. - Release and Changelog Updates (release readiness): bump version to stable and document DISCO improvements. Commits: 4d8755b5a16e2f53bff24b7cfb5f3e13f1dc68b6; 41293968f6ccf50471081279d651ca46b70b2a76. - Resample Module Precision and Cleanup (code quality and consistency): refactor numeric literals and ensure consistent floating-point handling. Commit: 8680e023c300a0e32ce103373bb432af4a5eec8d. - Internal Training Script Refactor (dev tooling): restructure training script for testing/debugging and update notebook run counts. Commit: 60aea8081bfc33991cce3349878d310f6adbd496. Major bugs fixed - DISCO Convolution Normalization Fixes: ensured normalization sums to 1 and switched to 1-norm for both transpose and standard cases; distributed convolution bug fixes. Commits: 39298ffe1d5aea9f8a8f5d2d4d9ec487abb05b83; 856a0f180b176e3a32e71fa86ee46fc188ff6be2; d81fbd347e9090c8ed1fe721c0fb3daaecbef1b1; 87d9bfdc39f9707fcda7bd2f9b8b1cd574f3e89c. - Pole-edge Resampling Robustness: extended inputs near poles and adjusted grid parameters to improve robustness in edge cases. Commit: 96a2b54689e9458f6e03a21c2b5c0f573f31c573. - (Additional accuracy improvements captured in the above items.) Overall impact and accomplishments - This release strengthens numerical stability and expands developer-facing capabilities with minimal disruption to users. The DISCO normalization fixes improve cross-device accuracy and consistency in distributed setups, while new LSNO and Morlet options enable broader, more efficient feature representations. Resampling robustness and precision enhancements reduce grid-related aliasing and edge-case failures. The upgrade to a stable release with a comprehensive changelog improves deployment confidence and observability. Technologies and skills demonstrated - Numerical methods and precision management (double precision tensors, 1-norm normalization), distributed computation robustness, Zernike polynomial basis integration, upsampling strategy design, Morlet filter optimization with Hann window, API cleanup and configuration ergonomics, testing/debug tooling, and release discipline.
January 2025
14 Commits • 5 Features
Jan 1, 2025January 2025 — NVIDIA/torch-harmonics monthly summary Key features delivered and notable fixes (business value in parentheses): - LSNO: Zernike Basis, Upsampling Options, and API Cleanup (new Zernike filter basis; multiple upsampling methods; cleaned parameter ordering; deprecated rank option removed). This expands modeling capabilities and simplifies usage for downstream pipelines. Commits: b6b2bce378df4f451985ab544cf4e7cc82bb941c; 3350099a70011bf0acb3dc96d056eee19ef9f0e7; 7126fb9a1af85e7ffb4f544fb3853917a6111255. - Morlet Filter Basis Enhancement (efficiency and accuracy): switch to Hann window for Morlet basis; updated defaults and related code. Commit: 780fd1435844b5ce672bfc40d0a83576e9e62824. - Psi Tensor Precision and Theta_cutoff Adjustment (accuracy and aliasing mitigation): increase psi tensor precision to double; add fudge factor for theta_cutoff to mitigate aliasing near poles. Commit: 15d0750cfc6a9c651a4020b942436c3613b5c90d. - Release and Changelog Updates (release readiness): bump version to stable and document DISCO improvements. Commits: 4d8755b5a16e2f53bff24b7cfb5f3e13f1dc68b6; 41293968f6ccf50471081279d651ca46b70b2a76. - Resample Module Precision and Cleanup (code quality and consistency): refactor numeric literals and ensure consistent floating-point handling. Commit: 8680e023c300a0e32ce103373bb432af4a5eec8d. - Internal Training Script Refactor (dev tooling): restructure training script for testing/debugging and update notebook run counts. Commit: 60aea8081bfc33991cce3349878d310f6adbd496. Major bugs fixed - DISCO Convolution Normalization Fixes: ensured normalization sums to 1 and switched to 1-norm for both transpose and standard cases; distributed convolution bug fixes. Commits: 39298ffe1d5aea9f8a8f5d2d4d9ec487abb05b83; 856a0f180b176e3a32e71fa86ee46fc188ff6be2; d81fbd347e9090c8ed1fe721c0fb3daaecbef1b1; 87d9bfdc39f9707fcda7bd2f9b8b1cd574f3e89c. - Pole-edge Resampling Robustness: extended inputs near poles and adjusted grid parameters to improve robustness in edge cases. Commit: 96a2b54689e9458f6e03a21c2b5c0f573f31c573. - (Additional accuracy improvements captured in the above items.) Overall impact and accomplishments - This release strengthens numerical stability and expands developer-facing capabilities with minimal disruption to users. The DISCO normalization fixes improve cross-device accuracy and consistency in distributed setups, while new LSNO and Morlet options enable broader, more efficient feature representations. Resampling robustness and precision enhancements reduce grid-related aliasing and edge-case failures. The upgrade to a stable release with a comprehensive changelog improves deployment confidence and observability. Technologies and skills demonstrated - Numerical methods and precision management (double precision tensors, 1-norm normalization), distributed computation robustness, Zernike polynomial basis integration, upsampling strategy design, Morlet filter optimization with Hann window, API cleanup and configuration ergonomics, testing/debug tooling, and release discipline.
December 2024
13 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/torch-harmonics. Delivered LSNO/SFNO enhancements and an optimized SWE prediction training workflow with integrated power spectrum analysis, including an equiangular default grid and pole-aware latitude handling. Implemented a kernel basis refactor with a new FilterBasis data structure, added support for multiple basis types, and introduced Disk Morlet/Morlet-like basis options to broaden convolution capabilities. Completed release notes and versioning updates to clearly reflect patches and new releases. Fixed critical bugs related to scale factor logic and latitude grid handling in SHT routines, along with several minor stability improvements. Overall impact: higher-quality SWE predictions, more flexible experimentation with basis configurations, and improved maintainability and release traceability. Technologies/skills demonstrated: Python-based training pipelines, spherical harmonic transforms (SHT), LSNO/SFNO architectures, power spectrum analysis, API/data-structure design (FilterBasis), and release engineering (versioning and changelogs).
13 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/torch-harmonics. Delivered LSNO/SFNO enhancements and an optimized SWE prediction training workflow with integrated power spectrum analysis, including an equiangular default grid and pole-aware latitude handling. Implemented a kernel basis refactor with a new FilterBasis data structure, added support for multiple basis types, and introduced Disk Morlet/Morlet-like basis options to broaden convolution capabilities. Completed release notes and versioning updates to clearly reflect patches and new releases. Fixed critical bugs related to scale factor logic and latitude grid handling in SHT routines, along with several minor stability improvements. Overall impact: higher-quality SWE predictions, more flexible experimentation with basis configurations, and improved maintainability and release traceability. Technologies/skills demonstrated: Python-based training pipelines, spherical harmonic transforms (SHT), LSNO/SFNO architectures, power spectrum analysis, API/data-structure design (FilterBasis), and release engineering (versioning and changelogs).
December 2024
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Quality Metrics
Correctness88.8%
Maintainability86.6%
Architecture84.0%
Performance78.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAJinjaJupyter NotebookMarkdownPythonShellTOMLYAML
Technical Skills
3D Scene UnderstandingC++CI/CDCUDACUDA programmingChangelog ManagementCode FormattingCode MaintenanceCode RefactoringComputer VisionCopyright Notice UpdateData ResamplingData VisualizationDataset HandlingDeep Learning
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