Sara Elkerdawy is a Member of Technical Staff in AI with over a decade bridging academic research and engineering practice, holding a PhD-focused background in computing science and 8+ years of hands-on industry experience. She specializes in computer vision, CNN model compression, and deployable ML systems for cloud and edge, with applied experience in traffic monitoring, smart-city surveillance, and ADAS. Her doctoral work introduced novel static, dynamic, and layer-wise pruning approaches and explored self-distillation for vision transformers, reflecting a focus on latency- and data-efficient models. With 10+ publications in top venues (CVPR, ACCV, IROS) and practical contributions at organizations from Huawei to Dexmata, she consistently translates state-of-the-art research into production-ready solutions. Colleagues rely on her ability to close the gap between cutting-edge methods and real-world constraints, especially hardware-aware optimizations that yield measurable latency and FLOP reductions.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Department of Computing Science, Doctor of Philosophy - PhD Department of Computing Science at University of Alberta
Bachelor of Science (BS) Computer Science, Bachelor of Science (BS) Computer Science at Faculty of Computers and Information - Cairo University
Computer Vision &Machine Learning, Computer Vision &Machine Learning at INRIA SUMMER SCHOOL
Master of Science (MS) Computer Vision - Computer Science, Master of Science (MS) Computer Vision - Computer Science at Center for Informatics Science - Nile University
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Sara Elkerdawy - Member Of Technical Staff AI at Dexmata