Naomi O.

Responsibilities:

• Contributed Production-level C++ fixes to Microsoft's ONNX Runtime, focusing on correctness, full specification compliance ,and performance optimizations.

• Designed and implemented a spec-compliant generic mechanism for all Reduce operators, fixing correctness issues for empty-axes cases and adding optimized fast-paths for operators that do not require pre/post-processing.

• After implementing the fix in the CPU Execution Provider, I identified that other EPs still exhibited the incorrect behavior and opened a formal Issue recommending aligning them with the corrected CPU implementation.

• Added full broadcasting support to RMSNormalization and LayerNormalization, implementing a complete spec-compliant solution while preserving the previous partial implementation as an optimized fast-path for common model cases.

• Developed comprehensive test coverage for all fixes, including targeted GTest unit tests and large-scale fuzz testing that generated tens of thousands of randomized shape combinations to validate operator correctness.

• Collaborated with Microsoft maintainers throughout the contribution process, including diagnosing CI failures, addressing code review feedback, running linters, and ensuring all changes aligned with ONNX Runtime’s design and coding standards.

• Optimized deep learning models using ONNX Runtime, performing CPU/GPU performance profiling (latency, throughput, batch size) and applying techniques such as quantization, pruning, and knowledge distillation—while evaluating their impact on model accuracy and computational efficiency.