Bracha leah O.

Responsibilities:

• Conducted applied research in quantization and performance optimization for deep-learning models using OpenVINO. Evaluated the impact of precision modes, dynamic shapes, and optimization passes on inference efficiency. Measured latency, throughput, and memory footprint across hardware configurations to understand trade-offs between accuracy and performance.

• Executed YOLOv7 object-detection experiments, including preprocessing, model conversion, inference benchmarking, and systematic performance validation. Analyzed runtime behavior across CPU and GPU devices and used profiling tools to visualize bottlenecks and guide optimization choices.

• Support for aten::lstmcell Implemented full operator support for aten::lstmcell within the OpenVINO PyTorch Frontend. Developed translator logic, shape-inference rules, internal validation steps, and graph-construction flows. Ensured correct handling of dynamic shapes, hidden-state initialization, and multi-directional scenarios. Verified compatibility with existing OpenVINO operations and integration with the model-conversion pipeline.

• Support for prim::tolist Added support for the prim::tolist operator, enabling correct lowering of list-conversion operations encountered in TorchScript/FX graphs. Built translator logic to manage tensor-to-list transformations, validated type behavior, and ensured consistent handling across dynamic inputs and nested list structures.

• Debugged complex TorchScript and FX graphs, tracing conversion failures through multiple layers of the model-loading pipeline. Identified unsupported patterns, resolved mismatches between PyTorch and OpenVINO operator behavior, and refined graph-building logic to ensure successful end-to-end model conversion.

• Developed unit tests and layer-test suites to validate operator correctness on CPU and GPU. Designed test coverage for diverse tensor shapes, data types, and dynamic-dimension scenarios. Verified numerical equivalence between PyTorch execution and OpenVINO inference, ensuring robustness across the operator infrastructure.

• Solved algorithmic challenges in C, C++, and Python, applying data-structure fundamentals such as arrays, linked lists, hash maps, dynamic programming, recursion, and bitmasking. Performed complexity analysis, debugging, and unit-test development to ensure correctness and performance of implemented solutions.