
Contributed to the chanzuckerberg/cz-benchmarks repository by developing a cross-species analysis feature for the CLI, enabling multi-dataset benchmarking across species. Enhanced the CI/CD pipeline to automate building and pushing the transcriptformer Docker image to ECR, improving container delivery reliability. Introduced a Python-based tool for comparing benchmarking metrics across runs, supporting both JSON and YAML formats for standardized evaluation. Addressed key bugs by correcting clustering metric calculations and ensuring reproducible label predictions with deterministic machine learning models. Demonstrated expertise in Python, Docker, and CI/CD workflows, with a focus on bioinformatics data integration, robust benchmarking, and reproducible analytical pipelines.
May 2025: Key features delivered include cross-species task for cz-benchmarks CLI enabling multi-dataset cross-species analysis; CI/CD workflow updated to build/push transcriptformer image to ECR; and a cross-run metrics comparison tool to standardize benchmarking across runs. Major bugs fixed include correcting silhouette score computation for clustering evaluation, ensuring reproducibility in label prediction via deterministic RandomForestClassifier, and stabilizing Docker image builds for transcriptformer and UCE. Overall impact: enhanced cross-dataset analytical capabilities, reliable CI/CD and container delivery, and robust benchmarking with reproducible metrics and cross-run comparisons. Technologies/skills demonstrated: Python scripting, scib_metrics integration, machine learning reproducibility, Docker, GitHub Actions, ECR, CLI extensions, and multi-format metric comparisons (JSON/YAML).
May 2025: Key features delivered include cross-species task for cz-benchmarks CLI enabling multi-dataset cross-species analysis; CI/CD workflow updated to build/push transcriptformer image to ECR; and a cross-run metrics comparison tool to standardize benchmarking across runs. Major bugs fixed include correcting silhouette score computation for clustering evaluation, ensuring reproducibility in label prediction via deterministic RandomForestClassifier, and stabilizing Docker image builds for transcriptformer and UCE. Overall impact: enhanced cross-dataset analytical capabilities, reliable CI/CD and container delivery, and robust benchmarking with reproducible metrics and cross-run comparisons. Technologies/skills demonstrated: Python scripting, scib_metrics integration, machine learning reproducibility, Docker, GitHub Actions, ECR, CLI extensions, and multi-format metric comparisons (JSON/YAML).

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