Omid Saremi, PhD
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Omid Saremi, Phd is a highly experienced machine learning/AI scientist with over 14 years of experience, PhD, postdoctoral research, and industry combined. He holds a BSc degree in Electrical Engineering with a focus on (array) signal processing using statistical modeling. Omid has extensive software development and design experience, with expertise in Python Machine learning ecosystem. He has worked at renowned companies like Apple, Verizon Labs, DecisionNext, and GEICO. Omid is known for his ability to deploy in distributed environments and has presented invited discussions on original research. He is also a contributor to peer-review journals.
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Machine learning/AI scientist. More than 14 years of experience, PhD, postdoctoral research and industry combined. An accomplished theoretical high energy physicist by training. BSc degree in Electrical Engineering with focus on (array) signal processing, using statistical modeling. Past industry/research experience includes Machine learning/Deep learning, Monte Carlo methods and quantitative mathematical modeling. Extensive software development and design experience. Expert in Python Machine learning ecosystem. Expert knowledge and practical experience with large-scale recommender systems, their algorithm design, implementation based on cutting edge academic research. Deployment in distributed environments. More recent areas of focus include search/relevance and information retrieval (machine learning/deep learning for ranking). Have presented invited talks on original research at numerous international venues, conferences and workshops. Referee for a number of distinguished peer-review journals. Github Twitter @Omid_Sar

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Work Experience

1 Apple Park Way, Cupertino, California, 95014, US
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Omid Saremi, PhD's Professional Milestones

  • Machine Learning Research Scientist (2018-01-01~2023-02-01): Developed cutting-edge machine learning models, revolutionizing data analysis and shaping the future of the machine learning industry.
  • Data scientist (2016-01-01~2017-05-01): Conducted data-driven analysis to inform strategic decision-making and drive business growth.
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University of Toronto
University of Toronto

Theoretical High Energy Physics (string Theory)