February 2026 monthly summary: Focused on expanding sparse matrix capabilities in the pytensor Numba backend. Implemented HStack/VStack for sparse matrices with shape validation, CSR/CSC column/row scaling (ColScaleCSC/RowScaleCSC), advanced sparse indexing primitives (GetItemList, GetItem2Lists, GetItem2d, GetItemScalar), sparse diagonal operations (Diag, square_diagonal), and sparse negation with a structured_elemwise refactor, along with performance improvements for sparse dot products and format conversions. These changes improve throughput, reduce memory overhead, and unlock broader modeling capabilities for production workloads.

Month: 2026-01 — PyTensor (pymc-devs/pytensor) performance and capability expansion through Numba backend sparse matrix support. Key features delivered include dense-to-sparse conversion, sparse dot product with transpose, overloads for the T attribute, and conversions (toarray, tocsr), plus gradients for structured dot products on CSR/CSC and sparse summation along axes. Comprehensive test coverage added to verify correctness and consistency across SparseFromDense, StructuredDotGrad, and SpSum paths.

Month: 2025-09 — Focused feature delivery and test quality improvements in the pymc-devs/pymc repo. Key feature delivered: targeted variable sampling in mock_sample using var_names, enabling precise control over which variables appear in the generated InferenceData for testing. This included a new test to verify var_names functionality, improving regression safety and test reliability. No major bugs reported this month; emphasis on delivering a robust, testable feature and reducing debugging time. Overall impact: clearer validation paths for variable selection in inference workflows, faster feedback on changes affecting test data composition, and strengthened confidence in model testing pipelines. Technologies/skills demonstrated: Python, PyMC, InferenceData handling, test-driven development, commit hygiene, and CI-ready implementation.