Developer with hands-on experience working on complex systems, able to quickly dive into existing codebases and integrate efficiently into large projects. Strong analytical thinking, independent learning skills, and the ability to solve system-level issues while designing and developing at a high level.
Deep debugging and kernel-level contributions to ONNX Runtime

Mentored by: Mobileye
Mentors:
Embedded Systems Bootcamp 2025 (Embedded)
Responsibilities:
Analyzed ONNX Runtime source (C++/Python) to understand and improve optimization flows, quantization mechanisms (static/dynamic), and operators such as ConvInteger and MatMulInteger.
Analyzed quantization logic (formulas, differences between symmetric/asymmetric, static/dynamic behavior).
Investigated the quantization structure in ORT – examining how Q/DQ layers are created, and the flow of tensor registration versus the actual quantization flow.
Examined the implementation of quantization in the source code – the model’s runtime behavior after quantization, understanding the Graph Optimization process, and writing documentation explaining the mechanisms and overall system flow.
Investigated and resolved issues by debugging errors, using Netron to view the ONNX graph, and finding the root cause.
Added tests for operators to cover input/output scenarios and verify correct behavior after kernel code fixes and changes.
Measured and compared FP32 vs INT inference performance (profiling, CPU time, wall-clock time) to analyze quantization impact on latency. Example.

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On-prem intranet system for managing and editing challenge exams, in active daily use.
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Working Proficiency