Reza Zadeh

CEO of Matroid & Stanford Professor

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  • California, United States

Reza Zadeh, a notable figure in the field of Machine Learning and AI, stands out as the CEO of Matroid and an Adjunct Professor at Stanford University. His journey in computational sciences began at the University of Waterloo, leading to significant contributions to Google’s AI research team. Zadeh's academic prowess was further highlighted by his doctoral thesis at Stanford, earning him the Gene Golub Outstanding Thesis Award.

At Twitter, Zadeh was instrumental in developing Machine Learning algorithms, notably for the "Who to Follow" feature, and co-created key components in Apache Spark. His role as an educator at Stanford, where he teaches Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms, has earned him the Best Instructor Award, reflecting his influence on the next generation of tech leaders.

In the industry, Zadeh's leadership at Matroid has pushed the boundaries of computer vision, notably winning the Princeton University ModelNet challenge and advancing medical diagnoses through neural networks. His co-authorship of Apache Spark’s MLlib and collaborations with industry giants like Databricks and Microsoft underscore his significant impact on the practical applications of machine learning. Reza Zadeh embodies the intersection of academic excellence and industry innovation, marking him as a key influencer in the evolving world of AI and Machine Learning.

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Reza’s previous speaking experience.

As a leading figure in the realms of machine learning and AI, Reza Bosagh Zadeh brings his extensive knowledge to global audiences, captivating them with insights into the transformative power of AI and distributed computing. His lectures and talks, ranging from academic circles at Stanford University to influential tech conferences, shed light on the complexities and potentials of AI in modern computing. Zadeh's experiences in pioneering projects at Google, Twitter, and Matroid, along with his educational contributions, provide a comprehensive perspective on the future of AI and its application in industry and academia.

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