hael/SIMPLE
Nov 2024 – Nov 2025
13 Months active
Languages Used
FortranPerlCMakeShellHTMLJavaScriptPython
Technical Skills
3D Image Analysis3D ReconstructionAb Initio CalculationsAlgorithm DevelopmentAlgorithm ImplementationAlgorithm Refinement
hael
PROFILE
Hael
Hans Elmlund developed and maintained the hael/SIMPLE repository, delivering a robust suite of tools for 3D reconstruction, clustering, and automated image analysis in cryo-electron microscopy. Over 13 months, he engineered workflows for ab initio and time-series analysis, implemented advanced clustering algorithms, and optimized particle picking pipelines. His work combined Fortran and Python to build scalable, high-performance routines for data processing, image segmentation, and memory management. By integrating configurable workflows, multi-criteria clustering, and distributed computing with SLURM, Hans improved automation, reliability, and reproducibility. The depth of his contributions is reflected in the repository’s enhanced accuracy, flexibility, and maintainability.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
367Total
Bugs
60
Commits
367
Features
142
Lines of code
56,383
Activity Months13
Your Network
44 people
Work History
November 2025
12 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) performance summary for hael/SIMPLE focused on accuracy, reliability, and testing enhancements across the analysis pipeline. Delivered key features: (1) Enhanced peak detection and segmentation (refpicker & thresholding) with new threshold methods, refined quanta calculations, and debugging aids; (2) Clustering enhancements enabling multi-criteria analysis through Cavgs via distance matrices and the new calc_sigstats_cavgs_dmat, plus development-mode distance matrices for testing; (3) Robust 2D image opening and memory management with improved error handling and resource deallocation. Major bugs fixed: robust fixes for opening2D-related issues, including corrections in picksegdiam and mini_stream_utils. Impact: higher pipeline accuracy, expanded testing coverage, and a scalable clustering framework that communicates multi-criteria information through distance matrices, reducing memory-related failures during large-scale analysis. Technologies/skills demonstrated: Python-based image processing, advanced thresholding/refpicker techniques, development/testing of distance-matrix-driven multi-criteria clustering, and careful memory/resource management.
12 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) performance summary for hael/SIMPLE focused on accuracy, reliability, and testing enhancements across the analysis pipeline. Delivered key features: (1) Enhanced peak detection and segmentation (refpicker & thresholding) with new threshold methods, refined quanta calculations, and debugging aids; (2) Clustering enhancements enabling multi-criteria analysis through Cavgs via distance matrices and the new calc_sigstats_cavgs_dmat, plus development-mode distance matrices for testing; (3) Robust 2D image opening and memory management with improved error handling and resource deallocation. Major bugs fixed: robust fixes for opening2D-related issues, including corrections in picksegdiam and mini_stream_utils. Impact: higher pipeline accuracy, expanded testing coverage, and a scalable clustering framework that communicates multi-criteria information through distance matrices, reducing memory-related failures during large-scale analysis. Technologies/skills demonstrated: Python-based image processing, advanced thresholding/refpicker techniques, development/testing of distance-matrix-driven multi-criteria clustering, and careful memory/resource management.
November 2025
October 2025
64 Commits • 28 Features
Oct 1, 2025October 2025: Delivered end-to-end enhancements to the micrograph processing and particle-picking pipeline in hael/SIMPLE, focusing on reliability, automation, and throughput. Implemented robust micrograph denoising and binarization, introduced a polymorphic data structure to support flexible pipelines, and shipped validation-driven tooling and outputs. Achieved seamless mini_stream integration with SegPICK, AbInitio2D, automatic pickrefs, and a reference-based workflow, including validation reporting and logging improvements. Also completed refactoring, UI cleanup, and targeted bug fixes with GCC compatibility improvements to stabilize the codebase for ongoing development.
October 2025
64 Commits • 28 Features
Oct 1, 2025October 2025: Delivered end-to-end enhancements to the micrograph processing and particle-picking pipeline in hael/SIMPLE, focusing on reliability, automation, and throughput. Implemented robust micrograph denoising and binarization, introduced a polymorphic data structure to support flexible pipelines, and shipped validation-driven tooling and outputs. Achieved seamless mini_stream integration with SegPICK, AbInitio2D, automatic pickrefs, and a reference-based workflow, including validation reporting and logging improvements. Also completed refactoring, UI cleanup, and targeted bug fixes with GCC compatibility improvements to stabilize the codebase for ongoing development.
September 2025
3 Commits • 2 Features
Sep 1, 2025Month 2025-09 — hael/SIMPLE: delivered feature enhancements for nanocrystal data analysis and introduced correlative analysis capabilities for image stacks, establishing more flexible, accurate, and scalable analytics workflows. Key work included refactoring and extending clustering and selection logic for nanocrystal data analysis, introducing a select_flag to switch between 'cluster' and 'class', and ensuring consistent state updates across data structures. Additionally, correlative analysis capabilities were added via cluster_stack and match_stacks routines to process and match image stacks, with corresponding updates to commander definitions and execution flow to support these new analysis paths for cryo-electron microscopy. These changes are underpinned by commits a45b27ce5a6ae616fa1be54e883ee257fad19f90, 8b7dc26ca760e47ae2c4e222b8cd40c46585bd6b, and abbf8605cc0849f8d1336235080670ae44e7639e, reflecting targeted improvements to nanocrystal processing and correlative imaging workflows.
3 Commits • 2 Features
Sep 1, 2025Month 2025-09 — hael/SIMPLE: delivered feature enhancements for nanocrystal data analysis and introduced correlative analysis capabilities for image stacks, establishing more flexible, accurate, and scalable analytics workflows. Key work included refactoring and extending clustering and selection logic for nanocrystal data analysis, introducing a select_flag to switch between 'cluster' and 'class', and ensuring consistent state updates across data structures. Additionally, correlative analysis capabilities were added via cluster_stack and match_stacks routines to process and match image stacks, with corresponding updates to commander definitions and execution flow to support these new analysis paths for cryo-electron microscopy. These changes are underpinned by commits a45b27ce5a6ae616fa1be54e883ee257fad19f90, 8b7dc26ca760e47ae2c4e222b8cd40c46585bd6b, and abbf8605cc0849f8d1336235080670ae44e7639e, reflecting targeted improvements to nanocrystal processing and correlative imaging workflows.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for hael/SIMPLE: Delivered a configurable ab initio2D workflow with user-defined stop stages and configurable clustering; integrated build improvements including LIBTIFF support and deployment-friendly version strings; stabilized GUI build by addressing compilation issues; added early-stage halt capability to support iterative testing and debugging. These changes reduce deployment friction, accelerate validation cycles, and improve reproducibility.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for hael/SIMPLE: Delivered a configurable ab initio2D workflow with user-defined stop stages and configurable clustering; integrated build improvements including LIBTIFF support and deployment-friendly version strings; stabilized GUI build by addressing compilation issues; added early-stage halt capability to support iterative testing and debugging. These changes reduce deployment friction, accelerate validation cycles, and improve reproducibility.
July 2025
9 Commits • 4 Features
Jul 1, 2025July 2025 monthly summary for hael/SIMPLE. Focused on delivering robust cavgs matching, enhanced clustering workflows, and integration flexibility to improve accuracy, exportability, and deployment readiness. Key features delivered: - Enhanced clustering and output for cavgs matching: consolidated clustering refactors and output capabilities for simple_commander_cavgs; adopted two-phase approach initially, then streamlined to direct clustering; updated versioning; improved cluster distance calculation; enabled exporting matched clusters. - Robust matching with k-medoids and enhanced distance criteria: refactored match_cavgs to incorporate k-medoids for reference medoids; enhanced distance matrix calculation across multiple criteria; improved image data handling and parameter settings. - Fourier-Mellin based image matching: switched to Fourier-Mellin transform for correlation matrix calculations; integrated into matching criteria to boost accuracy and robustness. - Pre-defined clustering scoring/ranking via existing solutions: added have_clustering parameter to allow using an external clustering solution and pre-defined cluster assignments; updated versioning. Major bugs fixed: - Stability and correctness in matching/averaging: multiple bug fixes across modules, including updates to git version calls, data processing refinements, matrix manipulation, class sampling logic, and clustering index handling; ready for testing. - Additional targeted fixes: buggy match_cavgs for Afan refactor and corresponding corrections; correction to class sampling when partition=yes. Overall impact and accomplishments: - Increased matching accuracy and stability, reducing manual review through reliable export of matched clusters and robust clustering scoring. - Enhanced flexibility to plug in external clustering solutions, accelerating experimentation and deployment. - Clearer versioning and release readiness, with a modular architecture enabling future feature toggles and parameterization. Technologies/skills demonstrated: - Clustering algorithms: k-medoids; clustering scoring/ranking; integration of pre-defined clustering solutions. - Image matching techniques: Fourier-Mellin transform for robust correlation calculations. - Data processing and matrix manipulation: improved distance matrices and data handling. - Versioning discipline and bug fixing: systematic fixes, test readiness, and release-oriented changes.
9 Commits • 4 Features
Jul 1, 2025July 2025 monthly summary for hael/SIMPLE. Focused on delivering robust cavgs matching, enhanced clustering workflows, and integration flexibility to improve accuracy, exportability, and deployment readiness. Key features delivered: - Enhanced clustering and output for cavgs matching: consolidated clustering refactors and output capabilities for simple_commander_cavgs; adopted two-phase approach initially, then streamlined to direct clustering; updated versioning; improved cluster distance calculation; enabled exporting matched clusters. - Robust matching with k-medoids and enhanced distance criteria: refactored match_cavgs to incorporate k-medoids for reference medoids; enhanced distance matrix calculation across multiple criteria; improved image data handling and parameter settings. - Fourier-Mellin based image matching: switched to Fourier-Mellin transform for correlation matrix calculations; integrated into matching criteria to boost accuracy and robustness. - Pre-defined clustering scoring/ranking via existing solutions: added have_clustering parameter to allow using an external clustering solution and pre-defined cluster assignments; updated versioning. Major bugs fixed: - Stability and correctness in matching/averaging: multiple bug fixes across modules, including updates to git version calls, data processing refinements, matrix manipulation, class sampling logic, and clustering index handling; ready for testing. - Additional targeted fixes: buggy match_cavgs for Afan refactor and corresponding corrections; correction to class sampling when partition=yes. Overall impact and accomplishments: - Increased matching accuracy and stability, reducing manual review through reliable export of matched clusters and robust clustering scoring. - Enhanced flexibility to plug in external clustering solutions, accelerating experimentation and deployment. - Clearer versioning and release readiness, with a modular architecture enabling future feature toggles and parameterization. Technologies/skills demonstrated: - Clustering algorithms: k-medoids; clustering scoring/ranking; integration of pre-defined clustering solutions. - Image matching techniques: Fourier-Mellin transform for robust correlation calculations. - Data processing and matrix manipulation: improved distance matrices and data handling. - Versioning discipline and bug fixing: systematic fixes, test readiness, and release-oriented changes.
July 2025
June 2025
48 Commits • 13 Features
Jun 1, 2025June 2025 monthly summary for hael/SIMPLE focusing on Cluster CAVGS work: progressed from initial common-lines based similarity concepts to a more robust, multi-metric clustering and ranking framework, with pruning of infeasible approaches and groundwork for production-like testing.
June 2025
48 Commits • 13 Features
Jun 1, 2025June 2025 monthly summary for hael/SIMPLE focusing on Cluster CAVGS work: progressed from initial common-lines based similarity concepts to a more robust, multi-metric clustering and ranking framework, with pruning of infeasible approaches and groundwork for production-like testing.
May 2025
45 Commits • 8 Features
May 1, 2025May 2025 performance summary for hael/SIMPLE focusing on automated clustering uplift, robustness, and observable business value. Delivered a cohesive set of features around autoselect Cavgs, enhanced clustering via k-medoids, and a refactored utilities stack, complemented by an initial image alignment framework and improved progress reporting. Addressed key stability issues across clustering modules and improved reporting/diagnostics to support data-driven decision making.
45 Commits • 8 Features
May 1, 2025May 2025 performance summary for hael/SIMPLE focusing on automated clustering uplift, robustness, and observable business value. Delivered a cohesive set of features around autoselect Cavgs, enhanced clustering via k-medoids, and a refactored utilities stack, complemented by an initial image alignment framework and improved progress reporting. Addressed key stability issues across clustering modules and improved reporting/diagnostics to support data-driven decision making.
May 2025
April 2025
62 Commits • 36 Features
Apr 1, 2025April 2025 performance summary for hael/SIMPLE focused on advancing sampling, reconstruction quality, and spectral analysis across refine3D and ab initio workflows, with targeted bug fixes to improve stability and reproducibility. Major outcomes include enhancements to projection-balanced and frequency-informed sampling, memory-leak mitigation, deterministic and periodic fill-in sampling, and robust reporting. Architectural progress in autoselect cavgs (2D/3D) and power spectrum tooling enabled more accurate class averages and scalable clustering. Maintenance and refactor work reduced churn and prepared pipelines for chunk-based testing, strengthening overall reliability and business value. Key deliverables spanned: improved sampling strategies in refine3D_ato and refine3D_auto; memory leak fix; deterministic and periodic fill-in sampling in abinitio3D; initial Power Spectrum Analyzer and clustering (k-medoids, hierarchical); autoselect cavgs enhancements including ranking, PSPEC refactor, and support for arbitrary cluster counts; multiple maintenance/refactor efforts such as removing repetitive updates and adding the Split_stack utility.
April 2025
62 Commits • 36 Features
Apr 1, 2025April 2025 performance summary for hael/SIMPLE focused on advancing sampling, reconstruction quality, and spectral analysis across refine3D and ab initio workflows, with targeted bug fixes to improve stability and reproducibility. Major outcomes include enhancements to projection-balanced and frequency-informed sampling, memory-leak mitigation, deterministic and periodic fill-in sampling, and robust reporting. Architectural progress in autoselect cavgs (2D/3D) and power spectrum tooling enabled more accurate class averages and scalable clustering. Maintenance and refactor work reduced churn and prepared pipelines for chunk-based testing, strengthening overall reliability and business value. Key deliverables spanned: improved sampling strategies in refine3D_ato and refine3D_auto; memory leak fix; deterministic and periodic fill-in sampling in abinitio3D; initial Power Spectrum Analyzer and clustering (k-medoids, hierarchical); autoselect cavgs enhancements including ranking, PSPEC refactor, and support for arbitrary cluster counts; multiple maintenance/refactor efforts such as removing repetitive updates and adding the Split_stack utility.
March 2025
27 Commits • 6 Features
Mar 1, 2025March 2025 highlights for hael/SIMPLE: Delivered core time-series nanocrystal analysis enhancements, RMSD-based similarity analytics, kPCA denoising controls with an atomic-threshold toggle, and an integrated t-series reconstruction / refine3D_auto auto-pipeline. Implemented targeted bug fixes and stability improvements to widen automation and reliability of the workflow. Key business/value outcomes: - Improved accuracy and stability of time-series analysis and nano-crystal trajectory handling, enabling more reliable data-driven insights. - More robust, automated 3D reconstruction and refinement workflows, reducing manual tuning and turnaround time. - Enhanced noise suppression and configurable denoising (kPCA) with safe defaults and optional atomic-threshold disabling, improving downstream quality. Top deliverables and changes: - Core time-series nanocrystal analysis enhancements: core finder revisions, optional center-of-mass centering toggle, radial averaging experiments, and RMSD-based exploration. - RMSD-based time-series similarity analysis: all-pairs RMSD statistics, median-based sorting, and nearest-neighbor reporting. - kPCA denoising controls and atomic threshold toggle: dedicated denoising workflow with defaults and an option to disable thresholding in autorefine3D_nano. - tseries reconstruction and refine3D_auto pipeline: drafting/integration, staging workflow, and ongoing auto-refinement development. - Bug fixes and stability: general batch bugfix; refine3D_auto bug fixes and autoscaling corrections; projection-balanced sampling in refine3D_auto; deterministic map connectivity and diagnostics enhancements.
27 Commits • 6 Features
Mar 1, 2025March 2025 highlights for hael/SIMPLE: Delivered core time-series nanocrystal analysis enhancements, RMSD-based similarity analytics, kPCA denoising controls with an atomic-threshold toggle, and an integrated t-series reconstruction / refine3D_auto auto-pipeline. Implemented targeted bug fixes and stability improvements to widen automation and reliability of the workflow. Key business/value outcomes: - Improved accuracy and stability of time-series analysis and nano-crystal trajectory handling, enabling more reliable data-driven insights. - More robust, automated 3D reconstruction and refinement workflows, reducing manual tuning and turnaround time. - Enhanced noise suppression and configurable denoising (kPCA) with safe defaults and optional atomic-threshold disabling, improving downstream quality. Top deliverables and changes: - Core time-series nanocrystal analysis enhancements: core finder revisions, optional center-of-mass centering toggle, radial averaging experiments, and RMSD-based exploration. - RMSD-based time-series similarity analysis: all-pairs RMSD statistics, median-based sorting, and nearest-neighbor reporting. - kPCA denoising controls and atomic threshold toggle: dedicated denoising workflow with defaults and an option to disable thresholding in autorefine3D_nano. - tseries reconstruction and refine3D_auto pipeline: drafting/integration, staging workflow, and ongoing auto-refinement development. - Bug fixes and stability: general batch bugfix; refine3D_auto bug fixes and autoscaling corrections; projection-balanced sampling in refine3D_auto; deterministic map connectivity and diagnostics enhancements.
March 2025
February 2025
11 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for hael/SIMPLE: Delivered a set of end-to-end enhancements strengthening the time-series nanoparticle analysis pipeline, including CLI tooling, bug fixes, and analytics automation. The work focused on improving workflow automation, accuracy of lifetime calculations, and expanding data-rich insights through new analysis capabilities.
February 2025
11 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for hael/SIMPLE: Delivered a set of end-to-end enhancements strengthening the time-series nanoparticle analysis pipeline, including CLI tooling, bug fixes, and analytics automation. The work focused on improving workflow automation, accuracy of lifetime calculations, and expanding data-rich insights through new analysis capabilities.
January 2025
9 Commits • 3 Features
Jan 1, 2025January 2025 performance highlights for hael/SIMPLE: Delivered robust atom detection and masking improvements, enabling more accurate isolation of atomic features and preparing for integration in autorefine3D_nano. Implemented Center2D_nano centering workflow to streamline nanoparticle time-series processing with refined parameter handling. Enhanced time-series analyses for 2D/3D workflows, including diameter estimation refinements, automated masking, radial averaging, and improved reconstruction parameters, accelerating end-to-end analysis pipelines. Implemented the Oristats nano commander lifecycle — addition and cleanup — to stabilize execution flows and simplify maintenance. Collectively, these changes improve automation, accuracy, and reliability, reducing manual intervention and enabling faster, repeatable nanoparticle analyses.
9 Commits • 3 Features
Jan 1, 2025January 2025 performance highlights for hael/SIMPLE: Delivered robust atom detection and masking improvements, enabling more accurate isolation of atomic features and preparing for integration in autorefine3D_nano. Implemented Center2D_nano centering workflow to streamline nanoparticle time-series processing with refined parameter handling. Enhanced time-series analyses for 2D/3D workflows, including diameter estimation refinements, automated masking, radial averaging, and improved reconstruction parameters, accelerating end-to-end analysis pipelines. Implemented the Oristats nano commander lifecycle — addition and cleanup — to stabilize execution flows and simplify maintenance. Collectively, these changes improve automation, accuracy, and reliability, reducing manual intervention and enabling faster, repeatable nanoparticle analyses.
January 2025
December 2024
35 Commits • 14 Features
Dec 1, 2024December 2024 (Month: 2024-12) delivered targeted Abinitio3D and workflow enhancements in hael/SIMPLE to improve fidelity, performance, and scalability. Core sampling/state handling and symmetry improvements were paired with new data-integration capabilities and a major revision that moves trailing reconstruction averaging to real-space with automatic weighting. The month also hardened stability through bug fixes in state consistency, population reporting, and build/runtime reliability, while introducing analytics and automation features (Spearman correlation, automasking with ICM, and dynamic sampling controls) and a scalable distributed execution model. Business value was increased through higher-quality 3D reconstructions, faster and more reliable pipelines, and easier ingestion of large, multi-source datasets. Technologies/skills demonstrated included distributed computing with SLURM, UI integration for sampling controls, automasking and ICM techniques, and robust build/stability practices.
December 2024
35 Commits • 14 Features
Dec 1, 2024December 2024 (Month: 2024-12) delivered targeted Abinitio3D and workflow enhancements in hael/SIMPLE to improve fidelity, performance, and scalability. Core sampling/state handling and symmetry improvements were paired with new data-integration capabilities and a major revision that moves trailing reconstruction averaging to real-space with automatic weighting. The month also hardened stability through bug fixes in state consistency, population reporting, and build/runtime reliability, while introducing analytics and automation features (Spearman correlation, automasking with ICM, and dynamic sampling controls) and a scalable distributed execution model. Business value was increased through higher-quality 3D reconstructions, faster and more reliable pipelines, and easier ingestion of large, multi-source datasets. Technologies/skills demonstrated included distributed computing with SLURM, UI integration for sampling controls, automasking and ICM techniques, and robust build/stability practices.
November 2024
40 Commits • 21 Features
Nov 1, 2024Monthly summary for 2024-11 focused on delivering high-value features, stabilizing core workflows, and enabling advanced analytics in hael/SIMPLE. Key outcomes include enhancements to 3D auto-masking for more accurate reconstructions, modernization of the abinitio3D workflow with naming conventions and cross-validation readiness, advanced LP-auto/low-pass estimation across multi-volume analyses, and a developing ensemble/volume analytics framework to enable scalable volume analysis. Major features delivered and improvements: - 3D auto-masking enhancements with nonuniform 3D automasking using ICM to improve reconstruction fidelity. - Abinitio3D workflow modernization including naming convention updates, abinitio3D_parts restructuring, and cross-validated 3D reconstruction workflow with related file moves. - LP auto and low-pass estimation enhancements across abinitio3D, including default lp_auto settings and Nyquist-range testing; ongoing tuning and stability improvements. - Draft and mature ensemble/volume analysis framework (volume ensemble analyzer) with multi-volume analysis modes and volanalyze improvements for scalable volume analysis. Overall impact: The month delivered substantial technical progress toward higher fidelity 3D reconstructions, more robust multi-volume workflows, and scalable volume analysis capabilities, while laying groundwork for performance optimizations and deeper ML/regularization integrations. Technologies/skills demonstrated: ICM-based automasking, abinitio3D workflow design and cross-validation integration, LP estimation techniques (Nyquist/low-pass), multi-volume analysis, ensemble analytics, code hygiene and experimental scaffolding.
40 Commits • 21 Features
Nov 1, 2024Monthly summary for 2024-11 focused on delivering high-value features, stabilizing core workflows, and enabling advanced analytics in hael/SIMPLE. Key outcomes include enhancements to 3D auto-masking for more accurate reconstructions, modernization of the abinitio3D workflow with naming conventions and cross-validation readiness, advanced LP-auto/low-pass estimation across multi-volume analyses, and a developing ensemble/volume analytics framework to enable scalable volume analysis. Major features delivered and improvements: - 3D auto-masking enhancements with nonuniform 3D automasking using ICM to improve reconstruction fidelity. - Abinitio3D workflow modernization including naming convention updates, abinitio3D_parts restructuring, and cross-validated 3D reconstruction workflow with related file moves. - LP auto and low-pass estimation enhancements across abinitio3D, including default lp_auto settings and Nyquist-range testing; ongoing tuning and stability improvements. - Draft and mature ensemble/volume analysis framework (volume ensemble analyzer) with multi-volume analysis modes and volanalyze improvements for scalable volume analysis. Overall impact: The month delivered substantial technical progress toward higher fidelity 3D reconstructions, more robust multi-volume workflows, and scalable volume analysis capabilities, while laying groundwork for performance optimizations and deeper ML/regularization integrations. Technologies/skills demonstrated: ICM-based automasking, abinitio3D workflow design and cross-validation integration, LP estimation techniques (Nyquist/low-pass), multi-volume analysis, ensemble analytics, code hygiene and experimental scaffolding.
November 2024
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Quality Metrics
Correctness80.8%
Maintainability80.4%
Architecture78.8%
Performance65.8%
AI Usage20.2%
Skills & Technologies
Programming Languages
CMakeFortranHTMLJavaScriptPerlPythonShell
Technical Skills
3D Image Analysis3D ReconstructionAI-Assisted DevelopmentAb Initio CalculationsAlgorithm DesignAlgorithm DevelopmentAlgorithm ImplementationAlgorithm OptimizationAlgorithm RefactoringAlgorithm RefinementBackend DevelopmentBenchmarkingBioinformaticsBug FixBug Fixes
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