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hssingh

PROFILE

Hssingh

Harsh Singh developed and enhanced time-series causal discovery features for the Lancelot39/Causal-Copilot repository, focusing on scalable algorithm integration and robust benchmarking. Over three months, he implemented parallelized and GPU-accelerated algorithms such as PCParallel and PCMCI, integrated frameworks like DYNOTEARS, and expanded support for Granger causality and NTS-NOTEARS. Using Python, NumPy, and Pandas, Harsh refactored wrappers, improved data preprocessing utilities, and restored core workflow scripts to ensure maintainability and reliability. His work enabled faster experimentation, richer temporal analysis, and more configurable benchmarking, demonstrating depth in algorithm optimization and a strong emphasis on reproducible, business-critical data science workflows.

Overall Statistics

Feature vs Bugs

83%Features

Repository Contributions

9Total
Bugs
1
Commits
9
Features
5
Lines of code
24,625
Activity Months3

Work History

April 2025

3 Commits • 1 Features

Apr 1, 2025

April 2025 monthly summary for Lancelot39/Causal-Copilot. Delivered Time Series Benchmarking Enhancements and restored main script functionality, resulting in faster, more configurable benchmarking and a reliable end-to-end workflow for testing and reporting. These efforts directly support business-critical time-series analyses and improve overall product reliability.

March 2025

2 Commits • 1 Features

Mar 1, 2025

March 2025 — Key milestones delivered for Lancelot39/Causal-Copilot focused on expanding time-series causal discovery capabilities and strengthening pipeline reliability. Business value accrued includes richer temporal insights for decision-making, faster experimentation cycles, and improved maintainability for future features.

February 2025

4 Commits • 3 Features

Feb 1, 2025

February 2025: Delivered scalable causal-discovery features, expanded framework integrations, and strengthened testing/documentation. Key outcomes include parallel PC (PCParallel), DYNOTEARS integration with wrappers/docs, and PCMCI/VARLiNGAM implementations with updated evaluation utilities. Resulting in broader model coverage, improved performance, and a more maintainable, testable codebase.

Activity

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Quality Metrics

Correctness83.4%
Maintainability80.0%
Architecture83.4%
Performance72.2%
AI Usage24.4%

Skills & Technologies

Programming Languages

CSVNPYPythonShellText

Technical Skills

Algorithm ConfigurationAlgorithm ImplementationAlgorithm IntegrationAlgorithm OptimizationBenchmarkingCausal DiscoveryCausal InferenceCode RefactoringCode RevertingData AnalysisData PreprocessingData ScienceData SimulationDocumentationGPU Acceleration

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

Lancelot39/Causal-Copilot

Feb 2025 Apr 2025
3 Months active

Languages Used

PythonShellTextCSVNPY

Technical Skills

Algorithm ImplementationAlgorithm IntegrationCausal DiscoveryCausal InferenceData ScienceDocumentation