Title: Statistically and Numerically Efficient Independence Test

The big data is a well-known phenomenon in the modern world. The emerging discipline of data science has inspired a lot of discussion and debate in the scientific research communities, including the mathematical and statistical science community. Contributing to this discussion, in the first part of this talk, I will present a discussion as well as a selective survey on the landscape of data science, as it is forming its foundation. On the second part of this talk, I will present one of my own research, which addresses a particular issue in the enormous spectrum of data science. More specifically, we study how to generate a statistical inference procedure that is both computational efficient and having theoretical guarantee on its statistical performance. We present numerical comparisons with contemporary approaches to demonstrate its advantages.