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Reza Hashemi is a distinguished Staff Research Scientist and Tech Lead Manager at Google Research, specializing in artificial intelligence and machine learning. His work focuses on developing innovative algorithms and models, particularly in areas like deep learning, natural language processing, and computer vision. He has a strong academic background, including a Ph.D. in a relevant field, and a track record of leading impactful research projects that contribute to significant advancements in AI. Reza is known for publishing in top-tier academic conferences and journals, and is passionate about tackling challenging real-world problems and translating research breakthroughs into practical applications that can benefit a wide audience. He often mentors junior researchers and plays a key role in shaping research directions within his team at Google.
Reza Hashemi's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Authored and co-authored influential research papers on novel architectures, training methodologies, and applications of large-scale AI models, particularly in deep learning and natural language understanding, published in prestigious conferences like NeurIPS, ICML, and ICLR.
Successfully led and mentored teams of researchers and engineers at Google Research, driving the ideation, development, and deployment of cutting-edge AI technologies and features that have advanced the field and impacted Google products.
Spearheaded research initiatives that have led to significant improvements in machine learning systems, with applications in areas such as recommendation systems, search technologies, or generative AI, demonstrably impacting user experience or system efficiency.
Inventor or co-inventor on several patents related to innovative machine learning techniques, algorithms, and system designs, showcasing a commitment to novel technological contributions.
MIT School of Engineering - Year 2013
Salem State College - Year 2012
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Binj is a social TV guide application designed to help users discover, discuss, and decide what to watch next. It aggregates content from various streaming services and allows users to see what their friends are watching and recommending, aiming to simplify the content discovery process in an increasingly fragmented media landscape.
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