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Sean Dieterich is an accomplished Research Scientist and Software Engineer at Google, with a strong focus on machine learning frameworks and high-performance computing. He is a key contributor to the JAX ecosystem, specializing in areas like compiler technology, automatic differentiation, and scalable ML systems. Sean's work aims to empower researchers and engineers to build and deploy cutting-edge AI models more efficiently. He holds a Ph.D. and has a background in scientific computing and physics, which informs his approach to complex problem-solving in AI.
Sean Dieterich's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Significantly contributed to the development, optimization, and adoption of JAX, a high-performance numerical computation library, enabling breakthroughs in machine learning research and large-scale model training at Google and within the broader AI community.
Instrumental in developing and enhancing ML compiler technologies, such as XLA (Accelerated Linear Algebra), to accelerate TensorFlow and JAX computations on various hardware platforms including TPUs and GPUs.
Developed and implemented techniques and infrastructure that support the training of exceptionally large and complex machine learning models, pushing the boundaries of AI capabilities.
Authored and co-authored influential research papers presented at top-tier machine learning and systems conferences, contributing valuable insights to the scientific community.
San Diego State University-California State University
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