Yiqiao Zhong

STAT 709: Mathematical Statistics

This course is a PhD-level entry course for students in the statistics department. The goal is to offer a broad view of modern statistics, ranging from classical asymptotic theory to high-dimensional statistics. We cover many tools such as concentration inequalities, basic random matrix theory, basic information theory, shrinkage estimation, etc.

Here is the syllabus for Fall 2023.

If you are interested in taking this course, please contact me for further information. If you are taking this course, please use Canvas or Piazza for the most updated information.

STAT 453

This course provides an introduction to deep learning. Minimum knowledge is required, but you should check if you meet the prerequisites before enrolling. The course is largely accredited to Sebastian Raschka. Various lecture notes from previous years may exist on the internet, but you should check Canvas for the most updated version.

Previous teaching experience

• STATS 385: Analyses of Deep Learning (Fall 2019)
  

• ORF 307: Optimization (Spring 2018)
  

• ORF 245: Fundamentals of Statistics (Spring 2017, Fall 2017)
  

• ORF 411: Operations and Information Engineering (Fall 2016)
  

• ORF 360: Decision Modeling in Business Analytics (Spring 2016)
  

• ORF 527: Stochastic Calculus (Spring 2016)
  

• ORF 524: Statistical Theory and Methods (Fall 2015)