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Joseph Chang is a Professor in the Department of Statistics and Data Science at Yale University. His expertise lies in probability theory and its applications. He has extensively researched random walks, a stochastic process that describes a path consisting of a succession of random steps. In addition, he focuses on sequential analysis, which is a statistical method for analyzing data as it is collected. Another significant aspect of his research includes pattern recognition, a cognitive process that involves recognizing patterns and regularities in data. Furthermore, he has a keen interest in machine learning, a field that enables computers to improve their performance on tasks through experience. His work integrates theoretical foundations with practical applications, contributing significantly to advances in probability and statistical methodologies.
Administered via the Graduate School of Arts and Sciences (GSAS). GRE General is optional for PhD.