Matthew Sweeten is a Senior Applied Scientist with extensive experience in data science, natural language processing, and machine learning. Matthew earned a Ph.D. and M.A. in Political Science from the University of Rochester, where they developed a Multicycle Expectation-Conditional-Maximization algorithm for analyzing Supreme Court opinions. Previously, Matthew worked as an Insight Data Science Fellow and held roles at Pearson and Amazon, focusing on AI solutions for educational technology and advanced scientific applications. Currently, Matthew continues to apply their expertise in machine learning and data insights at Amazon.
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