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Marina Avisar is a distinguished Senior Research Scientist at NVIDIA, specializing in Artificial Intelligence, with a strong focus on Deep Learning and Computer Vision. Her work involves pioneering research and development of cutting-edge AI models and algorithms, particularly in areas like generative models (including diffusion models) and efficient deep learning. Marina is known for her contributions to advancing AI capabilities and has a rich background that includes impactful research positions at prominent technology companies like Intel, prior to NVIDIA. She earned her PhD in Electrical Engineering from Tel Aviv University, where her research concentrated on machine learning and signal processing. Marina is passionate about translating theoretical research into practical applications that solve real-world problems and frequently publishes her work in top-tier academic conferences.
Marina Avisar's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Currently leads and contributes to groundbreaking research in generative AI, diffusion models, and efficient deep learning, pushing the boundaries of AI technology at one of the world's leading AI companies.
Authored and co-authored numerous influential research papers presented at premier AI and machine learning conferences such as NeurIPS, CVPR, ICCV, and ICML, contributing significantly to the academic discourse in the field.
Completed her doctoral studies at Tel Aviv University, with a dissertation focused on advanced machine learning techniques and signal processing, establishing a strong theoretical foundation for her career in AI research.
Contributed to AI and computer vision research projects at Intel Labs, gaining valuable industry experience in applying machine learning to solve complex technological challenges before joining NVIDIA.
Recognized for her work on novel architectures and methodologies in generative AI, including significant contributions to the understanding and application of diffusion models for high-fidelity image and data generation.
Queen's University
Massachusetts Institute of Technology
RCC Institute of Technology
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