Long Nguyen Professor of Statistics, Department of Statistics, University of Michigan Director of Master's programs in Statistics Other affiliations: Professor of EECS, Department of Electrical Engineering and Computer Science (courtesy appt) Faculty member, Michigan Institute for Data Science Long-term member, Vietnam Institute for Advanced Study in Mathematics 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 2020: 10--11am, Thursdays, remotely (e.g., via Zoom).

Prospective PhD students: Please consider applying to Michigan and thank you for your interest. Admissions decision is made a graduate admissions committee, please see this link for further information.

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),
• SIAM Journal on Mathematics of Data Science (Associate Editor),
• Journal of Machine Learning Research (Action Editor, 2018--2020),
• 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 probabilistically 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 and spatiotemporal and functional patterns.

My motivation for all this came originally from an early and sustained interest in machine learning. A primary focus in our machine learning research is to develop more effective inference algorithms using variational, stochastic and geometric viewpoints.

Students [selfie in deserted Niagara Falls in November'16] [on a Phở day, March 2018] [@Ashley's] [in the time of COVID-19, May 2020]

Current PhD students:

• Sunrit Chakraborty
• Trong Dat Do
• Bach Viet Do (joint with Yang Chen)
• Rayleigh Lei
• Vincenzo Loffredo

• Yun Wei PhD Math (AIM) 2020; Postdoctoral fellow, SAMSI and Duke University
• Aritra Guha, PhD Stats 2020; Postdoctoral fellow, Duke University
• Mikhail Yurochkin PhD Stats 2018; Research scientist, IBM Research, Cambridge, MA and MIT-IBM Watson AI Lab
• Nhat Ho PhD Stats 2017; Postdoctoral fellow, University of California, Berkeley; now Assistant Professor, University of Texas, Austin
• Hossein Keshavarz Shenastaghi PhD Stats 2017; Postdoctoral fellow at the IMA, University of Minnesota, now Senior Researcher at General Motors, MI
• Zhaoshi Meng PhD EECS 2014; Senior Researcher, Vicarious, CA
• Arash Ali Amini Postdoctoral fellow 2011--2014; Assistant Professor, University of California, Los Angeles
• Vijay Manikandan Janakiraman PhD Mechanical Engineering 2013; Research Scientist, NASA's Ames Research
• Jian Tang Visiting CS PhD student from Peking University 2011--2013; Assistant Professor, Université de Montréal
• Kohinoor Dasgupta, PhD Stats 2012; Senior Biostatistician, Novartis India
• Cen Guo, PhD Stats 2012; Data Scientist, Uber, CA

• Ziyi Song (AMDP)
• Sijun Zhang (AMDP)
• Jawad Mroueh, MS 2019
• Bopeng Li, MS 2012; in Statistics PhD program, University of Michigan

• 2019--2020: Jingyi Jia (graduate student at UM Statistics)
• 2018--2019: Yingsi Jian (graduate student at Harvard), Jiayue Lu (graduate student at Univ of Southern California)
• 2017--2018: Jiahui Ji (graduate student at UM Biostatistics), Zui Chen (graduate student at Parsons School of Design, NYC)

• 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

• Learning from naturalistic driving encounters: Joint with Ding Zhao (mechanical engineering faculty at Carnegie Mellon University) and funded by Toyota Research Institute.
• Music theory: Joint with music theorists at Michigan, Sam Mukherji, Áine Heneghan, Nathan Martin and Rene Rusch, and UM linguist Steven Abney.
• Statistical Machine Learning reading group. This link contains a list of excellent papers discussed in a reading group formerly organized by a number of young(!) UM statisticians and machine learning researchers (2011--2016).
• 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.

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.