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]

• Office hours in Fall 2019: 10--11pm, Thursdays, WH 461 or by email appt

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.

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

Research interests

• Nonparametric Bayesian statistics
• Machine learning and optimization
• Hierarchical, mixture and graphical models
• Spatiotemporal and functional data analysis
• Stochastic, variational and geometric methods in statistical inference

Editorial boards

• Annals of Statistics (Associate Editor),
• Bayesian Analysis (Associate Editor),
• Journal of Machine Learning Research (Action Editor),
• SIAM Journal on Mathematics of Data Science (Associate Editor),
• Annals of the Institute of Statistical Mathematics (Associate Editor, 2015--2018)

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]

Current PhD students:

• Bach Viet Do (joint with Yang Chen)
• Aritra Guha
• Rayleigh Lei
• Vincenzo Loffredo
• Yun Wei (joint with Al Hero)

• Mikhail Yurochkin PhD 2018, Research scientist, IBM Research, Cambridge, MA and MIT-IBM Watson AI Lab
• 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; now Senior Researcher at General Motors, MI
• 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 2011--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

• Jawad Mroueh, MS 2019
• Bopeng Li, MS 2012; in Statistics PhD program, University of Michigan

• 2017--2018: Jiahui Ji (graduate student at UM Biostatistics), Zui Chen (graduate student at Parsons School of Design, NYC)
• 2018--2019: Yingsi Jian (graduate student at Harvard), Jiayue Lu (graduate student at Univ of Southern California)

• Giuseppe Di Benedetto Visiting PhD student from Oxford University, March--May 2018
• Federico Camerlenghi Postdoctoral visiting scholar, April--May 2016; Assistant Professor, University of Milano-Bicocca
• Hyun-Chul Kim, Visiting Scholar 2010--2011; Research Professor, Yonsei University, Korea

Some collaborative projects and links

• Music theory: Joint with music theorists at Michigan, Sam Mukherji, Áine Heneghan, Nathan Martin and Rene Rusch, and UM linguist Steven Abney.
• Learning from naturalistic driving encounters: Joint with Ding Zhao (mechanical engineering faculty at Carnegie Mellon University) and funded by Toyota Research Institute.
• Real time CO2 data assimilation and anomaly detection project. Led by Anna Michalak Lab at Carnegie Institution for Science and Michigan team.
• Big Data Summer Institute. Led by Bhramar Mukherjee at the University of Michigan. Exciting opportunity for computer science, mathematics and statistics undergraduates looking to find meaning in very large scale data.
• STATMOS: Research Network for Statistical Methods for Atmotspheric and Oceanic Sciences.
• Vietnam Institute for Advanced Study in Mathematics. An excellent place for mathematics and mathematical research in Hanoi.

A biased and recent sample

• On posterior contraction of parameters and interpretability in Bayesian mixture modeling. A. Guha, N. Ho and X. Nguyen. arXiv:1901.05078.
• Singularity structures and impacts on parameter estimation in finite mixtures of distributions. N. Ho and X. Nguyen. To appear, SIAM Journal on Mathematics of Data Science.
• Scalable inference of topic evolution via models for latent geometric structures. M. Yurochkin, Z. Fan, A. Guha, P. Koutris and X. Nguyen. Advances in Neural Information Processing Systems (NeurIPS) 32, 2019.
• Dirichlet simplex nest and geometric inference. M. Yurochkin, A. Guha, Y. Sun and X. Nguyen. Proceedings of the ICML, 2019.
• On functional aggregate queries with additive inequalities. M. Abo Khamis, R. Curtin, B. Moseley, H. Ngo, X. Nguyen, D. Olteanu and M. Schleich. Proceedings of PODS, 2019.
• Local inversion-free estimation of spatial Gaussian processes. H. Keshavarz, X. Nguyen and C. Scott. arXiv:1811.12602. To appear, Electronic Journal of Statistics.
• Robust estimation of mixing measures in finite mixture models. N. Ho, X. Nguyen and Y. Ritov. arXiv:1709.08094. To appear, Bernoulli.
• Learning models over relational data using sparse tensors and functional dependencies. M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu and M. Schleich.
• Conic scan-and-cover algorithms for nonparametric topic modeling. M. Yurochkin, A. Guha and X. Nguyen. Advances in NIPS 30, 2017.
• Multi-way interacting regression via factorization machines. M. Yurochkin, X. Nguyen and N. Vasiloglou. 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.
• Borrowing strength in hierarchical Bayes: posterior concentration of the Dirichlet base measure. X. Nguyen. Bernoulli, 22(3), 1535--1571, 2016.
• 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.
• Posterior contraction of the population polytope in finite admixture models. X. Nguyen. Bernoulli, 21(1), 618--646, 2015.

• More ...

Selected talk slides

• Parameter estimation and interpretability in Bayesian mixture models. Keynote talk, 12th Bayesian Nonparametrics Conference, Oxford, June 2019.
• 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. Keynote talk, 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.