Long Nguyen Assistant Professor, Department of Statistics, Dept of Electrical Engineering and Computer Science (by courtesy), University of Michigan Email: xuanlong@umich.edu Office: 456 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] [Biography]

• Statistical Machine Learning reading group
• Dept of Statistics Seminar Series

Research interests

• Nonparametric Bayesian statistics
• Variational and decentralized statistical inference
• Machine learning in networks and distributed systems
• Analysis of signals, texts, images and other spatio-temporal and functional data

A biased sample of papers

• Identifiability and optimal rates of convergence for parameters of multiple types in finite mixtures. N. Ho and X. Nguyen. Technical Report No. 536, Dept of Statistics, Univ of Michigan, January 2015.
• Borrowing strength in hierarchical Bayes: convergence of the Dirichlet base measure. X. Nguyen. To appear, Bernoulli, 2015. [Originally listed as Technical Report No. 532, Dept of Statistics, Univ of Michigan, January 2013.]
• Posterior contraction of the population polytope in finite admixture models. X. Nguyen. In press, Bernoulli.
• 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 Neural Information Processing Systems (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.
• 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. DOI:10.1007/s10463-013-0436-7.
• Bayesian inference as iterated random functions with applications to sequential inference in graphical models. A. A. Amini and X. Nguyen. Advances in Neural Information Processing Systems (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 Transactions 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

• Borrowing strength in hierarchical Bayes: convergence of the Dirichlet base measure. 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. [Slides 1 ] [Notes 2 ]
• 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.

Biographical information

• Ph.D, UC Berkeley, 2007; Postdoctoral fellow, SAMSI/ Duke University, 2007--2009
• Best Paper Award, 21st International Conference on Machine Learning, 2004
• Young Author's Best Paper Award by the IEEE Signal Processing Society, 2007
• Leon O. Chua Award for outstanding achievement in an area of nonlinear science, UC Berkeley, 2007
• CAREER Award, National Science Foundation, Division of Mathematical Sciences, 2014--2019
• Best Paper Award, 31st International Conference on Machine Learning, 2014

• Associate Editor, Bayesian Analysis

Students

• Nhat Ho
• Hossein Keshavarz (joint with Clay Scott)
• Mikhail Yurochkin

• Zhaoshi Meng (PhD 2014, joint with Al Hero; Senior Researcher, Vicarious, CA)
• Arash Ali Amini (Postdoctoral fellow 2011--2014; Assistant Professor, UCLA)
• 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; Researcher, Microsoft Research Asia, Beijing)
• Kohinoor Dasgupta (PhD 2012, joint with Vijay Nair and Stilian Stoev; Senior Biostatistician, Novartis India)
• Cen Guo (PhD 2012, joint with Tailen Hsing; Senior Analyst, United Airlines, Chicago)
• Bopeng Li (MS 2012; in Statistics PhD program, University of Michigan)
• Hyun-Chul Kim (Visiting Scholar 2010--2011; Research Professor, Yonsei University, Seoul)

Links

• Real time CO2 data assimulation and anomaly detection project Led by Anna Michalak Lab at Carnegie Institution for Science and Michigan team
• STATMOS: Research Network for Statistical Methods for Atmotspheric and Oceanic Sciences
• Pointers on nonparametric Bayesian statistics Collected by Peter Orbanz
• Summer School: Modern statistical methods in machine learning Vietnam Institute for Advanced Study in Mathematics, Summer 2012
• Lexicon in probability, statistics and machine learning: Xác suất, thống kê và học máy (in Vietnamese)