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Daniel Xiang is an Assistant Instructional Professor in the Department of Statistics at the University of Chicago. He is known for his innovative teaching and contributions to the field of statistics. His research focuses on multiple testing, selective inference, sparse-approximation signal detection, and empirical Bayes methodology. Xiang is dedicated to advancing statistical methods and providing students with a profound understanding of statistical principles and techniques. His work incorporates rigorous analytical approaches and real-world applications, equipping students with the skills necessary for effective data analysis. Daniel's commitment to education fosters an engaging learning environment, promoting collaboration and exploration among students. His efforts in academia emphasize the importance of empirical evidence and methodological soundness in statistical research. Daniel continues to contribute to the academic community through his teaching and research endeavors, aiming to inspire future statisticians.
University of Chicago • Chicago, IL
Involved in teaching courses in statistics and engaging in research activities.
Department of Philosophy