Scientific computing.
Performance engineering.

A faster number is only the beginning.

Scientific Python performance depends on more than a single function. Library versions, native dependencies, build configuration, and hardware all affect the result. A useful optimization must also preserve expected behavior.

My work in the Kunpeng Python ecosystem brings these concerns together: investigate the measurements, understand the relevant implementation, and test whether a proposed change holds up.

From measurements to reviewable changes.

CPU performance and regression analysis

Analyze NumPy and pandas workloads on ARM64 using benchmark results and implementation-level investigation. Compare baseline and modified builds, identify regression cases, and use ASV to organize workload-level evidence rather than relying on a single headline timing.

Optimization migration across library versions

Audit downstream optimizations during the pandas 2.x-to-3.x transition. Trace each change’s original purpose, compare upstream implementation and behavior, and classify changes as reusable, already absorbed upstream, or requiring adaptation and validation.

Correctness and comparable environments

Record build configuration, Python and library versions, native dependencies, and test scope alongside results. Separate numerical and behavioral checks from performance claims, and distinguish same-platform optimization gains from ARM/x86 comparisons.

Make the comparison explainable.

I distinguish same-platform optimization gains from cross-platform comparisons. I also inspect individual cases alongside aggregate summaries, because a favorable aggregate can coexist with important regressions.

This is a high-level professional-work summary. It does not reproduce proprietary source, internal benchmark results, product identifiers, or deployment details. It does not claim sole ownership of the wider ecosystem work.