Mu ZhuContact Information:
Mu Zhu

Mu Zhu personal website

Research interests

Mu's main research interests are statistical machine learning and multivariate analysis, with their applications in health informatics, bioinformatics, and data mining. He is currently working with his PhD students on problems having to do with transactional networks and protein structures.

In recent years, his work has focused on efficient sparse kernel machines for unbalanced classification, ensemble approaches for variable selection, and algorithms for making personalized recommendations. Primarily an applied statistician, Mu has also made some theoretical contributions. For example, he was the first person to discover the theoretical difference between the forward and backward algorithms of projection pursuit. The two algorithms were previously thought to be equivalent, and their nontrivial difference remained unknown in the statistics community for more than fifteen years until the publication of Mu's work in what is widely regarded as the top theoretical journal in the field of mathematical statistics.


Mu is a professor of statistics at the University of Waterloo. A Phi Beta Kappa graduate of Harvard University, he obtained his PhD from Stanford University. His research has received a prestigious Discovery Accelerator Supplement Award from the Natural Sciences and Engineering Research Council of Canada (NSERC). In 2012-13, Mu served as president of the Business and Industrial Statistics Section for the Statistical Society of Canada, and chaired the student paper competition committee for the Statistical Learning and Data Mining Section of the American Statistical Association.

Selected publications

  • 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.
University of Waterloo
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