Forthcoming Publications

. Signal-plus-noise matrix models: eigenvector deviations and fluctuations. Biometrika.

Publications

. Statistical inference on random dot product graphs: a survey. Journal of Machine Learning Research, 2018.

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. The Kato-Temple inequality and eigenvalue concentration with applications to graph inference. Electronic Journal of Statistics, 2017.

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. A nonparametric two-sample hypothesis testing problem for random dot product graphs. Bernoulli, 2017.

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. A semiparametric two-sample hypothesis testing problem for random dot product graphs. Journal of Computational and Graphical Statistics, 2017.

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. Community detection and classification in hierarchical stochastic blockmodels. IEEE Transactions on Network Science and Engineering, 2017.

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. Empirical Bayes estimation for the stochastic blockmodels. Electronic Journal of Statistics, 2016.

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. A limit theorem for scaled eigenvectors of random dot product graphs. Sankhya A, 2016.

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. Statistical inference on errorfully observed graphs. Journal of Computational Graphical Statistics, 2015.

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. Generalized canonical correlation analysis for classification. Journal of Multivariate Analysis, 2014.

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. Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding. Electronic Journal of Statistics, 2014.

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. Locality statistics for anomaly detection in time series of graphs. IEEE Transactions on Signal Processing, 2014.

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. Consistent latent position estimation and vertex classification for random dot product graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

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. On latent position inference from doubly stochastic messaging activities. Multiscale Modeling and Simulation, 2013.

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. Universally consistent vertex classification for latent position graphs. Annals of Statistics, 2013.

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. Attribute fusion in a latent process model for time series of graphs. IEEE Transactions on Signal Processing, 2013.

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. Generalized canonical correlation analysis for disparate data fusion. Pattern Recognition Letters, 2013.

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. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM Journal on Matrix Analysis and Applications, 2013.

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. A consistent adjacency spectral embedding for stochastic blockmodel graphs. Journal of the American Statistical Association, 2012.

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Preprints

. On a 'two truths' phenomenon in spectral graph clustering. Arxiv preprint., 2018.

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. On spectral embedding performance and elucidating network structure. Arxiv preprint., 2018.

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. A statistical interpretation of spectral embedding: the generalised random dot product graph. Arxiv preprint, 2018.

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. On estimation and inference in latent structure random graphs. Arxiv preprint, 2018.

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. A central limit theorem for classical multidimensional scaling. Arxiv preprint, 2018.

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. A central limit theorem for an omnibus embedding of random dot product graphs. Arxiv preprint, 2017.

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. Robust estimation from multiple graphs under gross error contamination. Arxiv preprint, 2017.

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Teaching

Fall 2018

  • Statistical inference on graphs EN.553.742; co-instructor with Avanti Athreya
    • graduate level course on topics in random matrix, graph theory, and statistics.
  • Applied statistics and data analysis (EN.553.413 and EN.553.613)
    • advanced undergraduates & graduate level course on (generalized) linear models
    • also offered in Fall 2013 through Fall 2017

Previous semesters

  • Applied statistics and data analysis II (EN.553.414 and EN.553.614)
    • graduate level course on semiparametric/non-parametric regression and generalized linear mixed effects models
    • offered in 2017 and 2018 (Spring semester)
  • Topics in statistical pattern recognition (EN.553.735)
    • graduate level course on probabistic theory of pattern recognition.
    • offered in Spring 2016
  • Topics in statistics (EN.553.730)
    • graduate level course on dimension reduction and high-dimensional data analysis
    • offered in Fall 2011

Contact

  • minh@jhu.edu
  • Whitehead 306E, 3400 N. Charles St, Baltimore, Maryland, 21218, USA