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This talk will be divided into two main parts. The first part introduces the importance and foundation of Experimental Design and demonstrates how statistical questions often lead to interesting mathematical problems. In particular, we will see how ideas from linear algebra, combinatorics, and optimization appear naturally when studying the design of experiments. Sometimes, people think of statisticians only after data have been collected. However, decisions made before data collection can affect the theoretical properties of estimators and statistical inference. I hope this talk will convey that statistics plays an important role even before any data are available. In the second part, I will briefly introduce the development and challenges of a growing field: the design and analysis of order-of-addition experiments. This type of experiment provides a natural setting to rethink how predictors should be encoded in linear models and reveals several interesting mathematical challenges. I will also share some recent work and ideas related to this topic. 

More Information: https://sites.google.com/view/jingwenhuang