Long Nguyen

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

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

Other affiliations:
Department of Electrical Engineering and Computer Science (by courtesy)
Michigan Institute for Data Science

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

Students in the applied Master's program: If you have questions about specific course work, please send email to the advising team (email address: stat-ms-ad@umich.edu) or set up an appointment with one of the advisors.

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

Nonparametric Bayesian statistics
Machine learning and optimization
Hierarchical, mixture and graphical models
Spatiotemporal and functional data analysis

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. In recent years I have gravitated toward 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 motivation for all this came originally from an interest in machine learning, which continues to be a major source of research interest. A primary focus in my machine learning research is to develop more effective inference algorithms using variational, stochastic and geometric viewpoints.

A biased sample of papers

Conic scan and cover algorithms for nonparametric topic modeling. M. Yurochkin, A. Guha and X. Nguyen. To appear, Advances in NIPS 30, 2017.
Multi-way interacting regression via factorization machines. M. Yurochkin, X. Nguyen and N. Vasiloglou. To appear, Advances in NIPS 30, 2017.
Multilevel clustering via Wasserstein means. N. Ho, X. Nguyen, M. Yurochkin, H. H. Bui, V. Huynh and D. Phung. Proceedings of the ICML, 2017.
In-database learning with sparse tensors M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu and M. Schleich. To appear, Proceedings of PODS, 2018.
Singularity structures and impacts on parameter estimation in finite mixtures of distributions. N. Ho and X. Nguyen. arXiv:1609.02655.
Geometric Dirichlet means algorithm for topic inference. M. Yurochkin and X. Nguyen. Advances in NIPS 29, 2016.
Scalable nonparametric Bayesian multilevel clustering. V. Huynh, D. Phung, S. Venkatesh, X. Nguyen, M. Hoffman and H. H. Bui. Proceedings of UAI, 2016.
On the consistency of inversion-free parameter estimation for Gaussian random fields. H. Keshavarz, C. Scott and X. Nguyen. Journal of Multivariate Analysis, 150, 245--266, 2016.
Convergence rates of parameter estimation for some weakly identifiable finite mixtures. N. Ho and X. Nguyen. Annals of Statistics, 44(6), 2726--2755, 2016.
On strong identifiability and convergence rates of parameter estimation in finite mixtures. N. Ho and X. Nguyen. Electronic Journal of Statistics, 10(1), 271--307, 2016.
Optimal change point detection in Gaussian processes. H. Keshavarz, C. Scott and X. Nguyen. To appear, Journal of Statistical Planning and Inference.
Borrowing strength in hierarchical Bayes: posterior concentration of the Dirichlet base measure. X. Nguyen. Bernoulli, 22(3), 1535--1571, 2016.
Posterior contraction of the population polytope in finite admixture models. X. Nguyen. Bernoulli, 21(1), 618--646, 2015.
Parallel feature selection inspired by group testing. Y. Zhou, C. Zhang, U. Porwal, H. Q. Ngo, X. Nguyen, C. Ré, and V. Govindaraju. Advances in NIPS 27, 2014.
Bayesian nonparametric multilevel clustering with group-level contexts. V. Nguyen, D. Phung, X. Nguyen, S. Venkatesh and H. H. Bui. Proceedings of the ICML, 2014.
Understanding the limiting factors of topic modeling via posterior contraction analysis. J. Tang, Z. Meng, X. Nguyen, Q. Mei and M. Zhang. Proceedings of the ICML, 2014.
Bayesian nonparametric modeling for functional analysis of variance. X. Nguyen and A. E. Gelfand. Annals of the Institute of Statistical Mathematics, 66(3), 496--526, 2014.
Bayesian inference as iterated random functions with applications to sequential inference in graphical models A. A. Amini and X. Nguyen. Advances in NIPS 26, 2013.
Convergence of latent mixing measures in finite and infinite mixture models. X. Nguyen. Annals of Statistics, 41(1), 370--400, 2013. [Corrections]
Sequential detection of multiple change points in networks: A graphical model approach. A. A. Amini and X. Nguyen. IEEE Transactions on Information Theory, 59(9), 5824--5841, 2013.
The Dirichlet labeling process for clustering functional data. X. Nguyen and A. E. Gelfand. Statistica Sinica 21(3), 1249--1289, 2011.
Inference of global clusters from locally distributed data. X. Nguyen. Bayesian Analysis, 5(4), 817--846, 2010.
Estimating divergence functionals and the likelihood ratio by convex risk minimization. X. Nguyen, M. J. Wainwright and M. I. Jordan. IEEE Trans on Information Theory, 56(11), 5847--5861, 2010.
On surrogate loss functions and f-divergences. X. Nguyen, M. J. Wainwright and M.I. Jordan. Annals of Statistics, 37(2), 876--904, 2009.
More ...

Selected talk slides

Elements of data science Summer School on Data Science, Vietnam Institute for Advanced Study in Mathematics, Hanoi and Ho Chi Minh, May 2017.
Multi-level clustering with contexts via hierarchical nonparametric Bayesian inference. Biostatistics Seminar, University of Michigan, October 2016.
Singularity structures and parameter estimation in finite mixture models. Workshop on Empirical Likelihood Methodology, National University of Singapore, June 2016.
Topic modeling with more confidence: a theory and some algorithms. Pacific-Asia Knowledge Discovery and Data Mining Conference, Ho Chi Minh, May 2015.
Borrowing strength in hierarchical Bayes: convergence of the Dirichlet base measure. 9th Bayesian Nonparametrics Conference, Amsterdam, June 2013.
Convergence of latent mixing measures in finite and infinite mixture models. Bayesian Nonparametrics Workshop at ICERM, Providence, September 2012.
Clustering problems, mixture models and Bayesian nonparametrics. VIASM Summer School, Hanoi, July 2012. [Additional notes ]
Message-passing sequential detection of multiple change points in networks. IEEE Symposium on Information Theory, Boston, July 2012.
Inference of functional clusters from non-functional data . Midwest Statistics Research Colloquium, Madison, March 2012.
Dirichlet labeling and hierarchical processes for clustering functional data . IMS-China Conference, Xi'an, July 2011.
Decentralized decision making with spatially distributed data . AI Seminar, University of Michigan, Oct 2009.
Surrogate loss functions, divergences and decentralized detection. Thesis Talk, UC Berkeley, May 2007.
Anomaly and sequential detection with time series data . Tutorial lectures given at Berkeley, 2006.

Students [group selfie]

Former students, postdocs and visitors

Nhat Ho PhD 2017, joint with Ya'acov Ritov; Postdoctoral fellow, University of California, Berkeley
Hossein Keshavarz PhD 2017, joint with Clay Scott; Postdoctoral fellow at the IMA, University of Minnesota
Federico Camerlenghi Postdoctoral visiting scholar 2016; Bocconi University, Italy
Zhaoshi Meng PhD 2014, joint with Al Hero; Senior Researcher, Vicarious, CA
Arash Ali Amini Postdoctoral fellow 2011--2014; Assistant Professor, University of California, Los Angeles
Vijay Manikandan Janakiraman PhD 2013, joint with Dennis Assanis; Research Scientist, NASA's Ames Research
Jian Tang Visiting PhD student from Peking University 2012--2013; Assistant Professor, Université de Montréal
Kohinoor Dasgupta, PhD 2012, joint with Vijay Nair and Stilian Stoev; Senior Biostatistician, Novartis India
Cen Guo, PhD 2012, joint with Tailen Hsing; Data Scientist, Uber, CA
Bopeng Li, MS 2012; in Statistics PhD program, University of Michigan
Hyun-Chul Kim, Visiting Scholar 2010--2011; Research Professor, Yonsei University, Korea

Bio [blurb]

Associate Editor, Bayesian Analysis (2014--)
Associate Editor, Annals of the Institute of Statistical Mathematics (2015--)
Program Committee's Area Chair, AISTATS (2015, 2017), ICML (2015, 2017), IJCAI (2016)
Formative education in Hai Phong (Vietnam), Bachelor's degree from POSTECH (Korea)
Master's degree from Arizona State University, apprenticing with Subbarao Kambhampati
Ph.D, University of California, Berkeley, advised by Michael Jordan and Martin Wainwright
Postdoctoral fellow at SAMSI and Duke University, mentored by Alan Gelfand and Jim Clark