Long Nguyen

Associate Professor, Department of Statistics, University of Michigan
Director of Master's programs in Statistics

Other affiliations:
Department of Electrical Engineering and Computer Science
Michigan Institute for Data Science
Vietnam Institute for Advanced Study in Mathematics (long-term member)

Email: xuanlong@umich.edu
Office: 461 West Hall, Phone: 734-763-3499, Fax: 734-763-4676

Mail Address: 439 West Hall, 1085 South University, Ann Arbor, MI 48109-1107

[Research] [Teaching] [Students] [Publications]

Students in the Master's programs in Statistics: Questions about specific course work should be directed to the advising team (email address: stat-ms-ad@umich.edu) or to your assigned faculty advisor. Please go to this page to this page set up an appointment with me or other advisors. Students in the dual Master's program are welcome to sign up for an appointment with me.

Prospective PhD students: Thank you for your interest. Admissions decision is made a graduate admissions committee, please see this link for further information. My apology if I am unable to respond to your enquiry due to the large volume of such emails.

Research interests

Editorial boards

Synopsis: Statistical inference is the computational process of turning data into statistics, prediction and understanding. I work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals.

I am particularly interested in a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. The spirit of Bayesian nonparametric statistics is to enable the kind of inferential procedures according to which both the statistical modeling and computational complexity may adapt to increasingly large and complex data patterns in a graceful and effective way. In this framework, stochastic processes and random measures, along with latent variable models such as mixture, hierarchical and graphical models figure prominently. My students and I seek to understand the interaction between statistical inference and the theory of optimal transport that arises in the learning of complex hierarchical models.

My motivation for all this came originally from an interest in machine learning, which continues to be an area of major activities. A primary focus in our machine learning research is to develop more effective inference algorithms using variational, stochastic and geometric viewpoints.

Fun statistics: number of papers I have written that has a giant's name in the title, as of twenty eighteen: Bayes = 6; Dirichlet = 4; Gauss = 2; Wasserstein = 1; Lyapunov = 1; Names I wish to appear on my paper's title: Blackwell, De Finetti, Riemann, Stein,

Students [selfie in deserted Niagara Falls in November] [on a Phở day] [@Ashley's]

Former students and postdocs Master's students Undergraduate honor thesis advisees Visitors

Some collaborative projects and links

A biased and recent sample

Selected talk slides

Last updated on September 7, 2015 by XuanLong Nguyen (Nguyễn Xuân Long)