Contact Information:
Mu Zhu
Research interests
Mu's initial research interest was dimension reduction. In the early years of his faculty career, he devoted much attention to efficient kernel machines for rare target detection and ensemble methods for variable selection. He also worked on algorithms for making personalized recommendations, and applications of machine learning to healthcare informatics.
While ensemble learning continued to captivate his curiosity, in more recent years Mu explored a hodgepodge of different topics—such as evaluation metrics, protein structures, transactional networks, and genetic epistasis. He also wrote a textbook for data science students. At present, he is studying various problems about dependence modeling, large covariance matrices, and generative neural networks.
Education/biography
Mu received his undergraduate degree from Harvard and his PhD from Stanford. He is an elected Fellow of the American Statistical Association and currently holds a University Research Chair at Waterloo.
Selected publications
- Zhu M (2023), Essential Statistics for Data Science, Oxford University Press.
- Zhu M (2014), "Making personalized recommendations in e-commerce," in Statistics in Action: A Canadian Outlook, J. F. Lawless, Ed., Chapman & Hall, pp. 259 - 268.
- Zhu M (2008), "Kernels and ensembles: Perspectives on statistical learning," The American Statistician, 62, pp. 97 - 109.
- Zhu M (2004), "On the forward and backward algorithms of projection pursuit," The Annals of Statistics, 32, pp. 233 - 244.