
Andy Tan developed core data ingestion, analytics, and backend abstraction features for the google/meridian repository, focusing on scalable, reproducible modeling and visualization workflows. He engineered a unified backend layer supporting both TensorFlow and JAX, enabling flexible numerical operations and probabilistic sampling. His work included robust data validation, time-aware analytics, and dynamic charting using Python, JAX, and Vega-Lite, ensuring reliable geospatial-temporal evaluation and organic media analysis. Andy addressed edge cases in spend analytics, improved serialization and testing, and maintained code quality through refactoring and documentation. His contributions deepened backend flexibility and data integrity, supporting maintainable, cross-backend machine learning pipelines.

Performance summary for 2025-10: Delivered JAX backend integration for prior_sampler in google/meridian, enabling stateful RNG via PRNGKeys and ensuring proper handling of JAX's stateless RNG within sampling methods; updated tests to reflect the switch from error to warning for set_random_seed in JAX. Also performed code formatting and readability cleanup in model.py and model_test.py to improve maintainability, with autoformatting committed. Overall, these changes improve reproducibility, reliability of sampling under JAX, and code quality for future enhancements.
Performance summary for 2025-10: Delivered JAX backend integration for prior_sampler in google/meridian, enabling stateful RNG via PRNGKeys and ensuring proper handling of JAX's stateless RNG within sampling methods; updated tests to reflect the switch from error to warning for set_random_seed in JAX. Also performed code formatting and readability cleanup in model.py and model_test.py to improve maintainability, with autoformatting committed. Overall, these changes improve reproducibility, reliability of sampling under JAX, and code quality for future enhancements.
2025-09 monthly performance: Delivered cross-backend Meridian consolidation with JAX support, enabling unified backend usage across visualization, testing, and sampling. Introduced backend-agnostic utilities and RNG handling to ensure consistent, cross-backend operation and reproducibility. Implemented runtime backend selection via MERIDIAN_BACKEND and serialization compatibility across components, simplifying cross-backend experiments and deployments. Enhanced Adstock and Hill calculations for cross-backend compatibility, including JAX boolean masking and fixed channel alignment. Expanded testing to validate both TensorFlow and JAX backends and adjusted tolerances to accommodate backend-specific behavior. Improved code quality and data serialization (e.g., prior_distributions and related utilities) to support stable, repeatable results across environments.
2025-09 monthly performance: Delivered cross-backend Meridian consolidation with JAX support, enabling unified backend usage across visualization, testing, and sampling. Introduced backend-agnostic utilities and RNG handling to ensure consistent, cross-backend operation and reproducibility. Implemented runtime backend selection via MERIDIAN_BACKEND and serialization compatibility across components, simplifying cross-backend experiments and deployments. Enhanced Adstock and Hill calculations for cross-backend compatibility, including JAX boolean masking and fixed channel alignment. Expanded testing to validate both TensorFlow and JAX backends and adjusted tolerances to accommodate backend-specific behavior. Improved code quality and data serialization (e.g., prior_distributions and related utilities) to support stable, repeatable results across environments.
August 2025 monthly summary: Implemented foundational backend abstraction enabling multi-backend support (JAX, TensorFlow) across Meridian core modules and centralized backend ops under meridian.backend, enhancing testability, maintainability, and future extensibility. Expanded analytics capabilities with Organic RF support across Analyzer, Hill curves, and plotting, with docs and tests updated. Other notable improvements include deterministic RNG seeding in prior_sampler and migration of components to the backend API (backend.Tensor usage, etc.).
August 2025 monthly summary: Implemented foundational backend abstraction enabling multi-backend support (JAX, TensorFlow) across Meridian core modules and centralized backend ops under meridian.backend, enhancing testability, maintainability, and future extensibility. Expanded analytics capabilities with Organic RF support across Analyzer, Hill curves, and plotting, with docs and tests updated. Other notable improvements include deterministic RNG seeding in prior_sampler and migration of components to the backend API (backend.Tensor usage, etc.).
Monthly summary for 2025-07 focusing on business value and technical achievements in google/meridian.
Monthly summary for 2025-07 focusing on business value and technical achievements in google/meridian.
April 2025 monthly summary focused on delivering richer channel insights, expanding organic media analysis, and ensuring release hygiene for Meridian. The work emphasized business value through time-aware visualizations, cross-channel comparisons, and maintainable release processes.
April 2025 monthly summary focused on delivering richer channel insights, expanding organic media analysis, and ensuring release hygiene for Meridian. The work emphasized business value through time-aware visualizations, cross-channel comparisons, and maintainable release processes.
March 2025 — google/meridian: Implemented data validation and integrity improvements, enhanced analytics capabilities, and improved visualization robustness. Focus areas included data integrity for geometry and coordinates, time-aware metrics for contribution analytics, and responsive Vega charts to fit dynamic containers. These changes increase reliability of data processing, enable time-based insights, and improve visualization UX for end users and business stakeholders.
March 2025 — google/meridian: Implemented data validation and integrity improvements, enhanced analytics capabilities, and improved visualization robustness. Focus areas included data integrity for geometry and coordinates, time-aware metrics for contribution analytics, and responsive Vega charts to fit dynamic containers. These changes increase reliability of data processing, enable time-based insights, and improve visualization UX for end users and business stakeholders.
February 2025 monthly summary focusing on bug fixes and stability improvements for google/meridian. Key contributions include addressing a critical edge-case in Budget Optimizer and strengthening test coverage, delivering business value through more reliable spend analytics.
February 2025 monthly summary focusing on bug fixes and stability improvements for google/meridian. Key contributions include addressing a critical edge-case in Budget Optimizer and strengthening test coverage, delivering business value through more reliable spend analytics.
January 2025 monthly summary focused on stabilizing national model evaluation in google/meridian by fixing a holdout filtering index bug, improving 1D time indexing handling, and expanding test coverage. No new features shipped this month; main work centered on bug fix with direct business value in model reliability and data integrity.
January 2025 monthly summary focused on stabilizing national model evaluation in google/meridian by fixing a holdout filtering index bug, improving 1D time indexing handling, and expanding test coverage. No new features shipped this month; main work centered on bug fix with direct business value in model reliability and data integrity.
December 2024 monthly summary for google/meridian focusing on delivering end-to-end data ingestion and analysis capabilities, stabilizing data integrity, and enabling geospatial-temporal evaluation at scale.
December 2024 monthly summary for google/meridian focusing on delivering end-to-end data ingestion and analysis capabilities, stabilizing data integrity, and enabling geospatial-temporal evaluation at scale.
Overview of all repositories you've contributed to across your timeline