Faculty

Carey Priebe

Professor

Research Interests

  • Computational Statistics
  • Kernel and Mixture Estimates
  • Statistical Pattern Recognition
  • Statistical Image Analysis
  • Dimensionality Reduction
  • Model Selection
  • Statistical Inference for High-Dimensional and Graph Data

Carey E. Priebe received a BS degree in mathematics from Purdue University in 1984, an MS degree in computer science from San Diego State University in 1988, and a PhD in information technology (computational statistics) from George Mason University in 1993. From 1985 to 1994, he worked as a mathematician and scientist in the US Navy research and development laboratory system. Since 1994 he has been a professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University.  He holds joint appointments in the Departments of Computer Science, Electrical and Computer Engineering,as well as the Center for Imaging Science, the Human Language Technology Center of Excellence, and the Whitaker Biomedical Engineering Institute. His research interests include computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, dimensionality reduction, model selection, and statistical inference for high-dimensional and graph data. He is a Senior Member of the IEEE, a Lifetime Member of the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and a Fellow of the American Statistical Association. He won an Office of Naval Research Young Investigator Award in 1995. He was recipient of the 2010 American Statistical Association Distinguished Achievement Award, the 2011 McDonald Award for Excellence in Mentoring and Advising, and in 2008 was named one of six inaugural National Security Science and Engineering Faculty Fellows.

Secondary Appointment: Electrical and Computer Engineering

Education
  • Ph.D. 1993, GEORGE MASON UNIVERSITY
Research Areas
  • COMPUTATIONAL statistics
  • Dimensionality reduction
  • Model selection
  • Statistical inference for high-dimensional and graph data
  • Statsitical pattern recognition
Awards
  • 2008:  National Security Science and Engineering Faculty Fellow - 2008
  • 2008:  Erskine Fellow - University of Canterbury - Christchurch - New Zealand - 2009
  • 2008:  Research Professor in the National Security Institute at the Naval Postgraduate School
  • 2008:  Robert B. Pond - Sr. - Excellence in Teaching Award - 2008
  • 2008:  Senior Member of the IEEE (Elected 2008)
  • 2007:  Elected Member of the International Statistical Institute(Elected 2007)
  • 2007:  Elected Vice President of the International Association for Statistical Computing - 2007-2009
  • 2006:  Exceptional Service to the American Statistical Association's Defense and Security Task Force - from Sallie Keller-McNulty - ASA President
  • 2006:  Keynote Speaker - 2006 Army Conference on Applied Statistics
  • 2002:  Fellow of the American Statistical Association (Elected 2002)
  • 2002:  National Security Agency Advisory Board Mathematics Panel
  • 2000:  ASEE Sabattical Leave Fellow
Journal Articles
  • Fishkind DE, Adali S, Patsolic HG, Meng L, Singh D, Lyzinski V, Priebe CE (2019).  Seeded graph matching.  Pattern Recognition.  87.  203-215.
  • Tang M, Priebe CE (2018).  Limit theorems for eigenvectors of the normalized Laplacian for random graphs.  Annals of Statistics.  46(5).  2360-2415.
  • Rosen MA, Dietz AS, Lee N, Jeng Wang I, Markowitz J, Wyskiel RM, Yang T, Priebe CE, Sapirstein A, Gurses AP, Pronovost PJ (2018).  Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion.  PLoS ONE.  13(10).
  • Shen C, Priebe C, Maggioni M, Vogelstein J (2018).  Discovering relationships across disparate data modalities.
  • Athreya A, Fishkind DE, Tang M, Priebe CE, Park Y, Vogelstein JT, Levin K, Lyzinski V, Qin Y, Sussman DL (2018).  Statistical inference on random dot product graphs: A survey.  Journal of Machine Learning Research.  18.  1-92.
  • Zheng D, Mhembere D, Vogelstein JT, Priebe CE, Burns R (2018).  FlashR: Parallelize and scale R for machine learning using SSDs.  Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP.  183-194.
  • Levin K, Roosta-Khorasani F, Mahoney MW, Priebe CE (2018).  Out-of-sample extension of graph adjacency spectral embedding.  35th International Conference on Machine Learning, ICML 2018.  7.  4632-4641.
  • Tang R, Ketcha M, Badea A, Calabrese ED, Margulies DS, Vogelstein JT, Priebe CE, Sussman DL (2018).  Connectome Smoothing via Low-rank Approximations.  IEEE Transactions on Medical Imaging.
  • Levin K, Athreya A, Tang M, Lyzinski V, Priebe CE (2017).  A central limit theorem for an omnibus embedding of multiple random dot product graphs.  IEEE International Conference on Data Mining Workshops, ICDMW.  2017-November.  964-967.
  • Lyzinski V, Levin K, Priebe C (2017).  On consistent vertex nomination schemes.
  • Tang M, Cape J, Priebe C (2017).  Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral.
  • Shen C, Priebe C, Vogelstein J (2017).  From distance correlation to multiscale generalized correlation.
  • Lyzinski V, Park Y, Priebe CE, Trosset M (2017).  Fast Embedding for JOFC Using the Raw Stress Criterion.  Journal of Computational and Graphical Statistics.  26(4).  786-802.
  • Qin Y, Priebe CE (2017).  Robust hypothesis testing via Lq-likelihood.  Statistica Sinica.  27(4).  1793-1813.
  • Rubin-Delanchy P, Priebe C, Tang M (2017).  The generalized random dot product graph.
  • Athreya A, Fishkind D, Levin K, Lyzinski V, Park Y, Qin Y, Sussman D, Tang M, Vogelstein J, Priebe C (2017).  Statisical inference on random dot product graphs: a survey.  Journal of Machine Learning Research.
  • Yoder J, Priebe CE (2017).  Semi-supervised k-means++.  Journal of Statistical Computation and Simulation.  87(13).  2597-2608.
  • Eichler K, Li F, Litwin-Kumar A, Park Y, Andrade I, Schneider-Mizell CM, Saumweber T, Huser A, Eschbach C, Gerber B, Fetter RD, Truman JW, Priebe CE, Abbott LF, Thum AS, Zlatic M, Cardona A (2017).  The complete connectome of a learning and memory centre in an insect brain.  Nature.  548(7666).  175-182.
  • Tang M, Athreya A, Sussman DL, Lyzinski V, Priebe CE (2017).  A nonparametric two-sample hypothesis testing problem for random graphs.  Bernoulli.  23(3).  1599-1630.
  • Patsolic H, Park Y, Lyzinski V, Priebe C (2017).  Vertex nomination via local neighborhood matching.
  • Levin K, Athreya A, Tang M, Lyzinski V, Priebe C (2017).  A central limit theorem for an omnibus embedding of random dot product graphs.
  • Tang R, Tang M, Vogelstein J, Priebe C (2017).  Robust estimation from multiple graphs under gross error contamination.
  • Mhembere D, Zheng D, Priebe CE, Vogelstein JT, Burns R (2017).  Knor: A NUMA-optimized in-memory, distributed and semi-external-memory k-means library.  HPDC 2017 - Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing.  67-78.
  • Yoder J, Priebe C (2017).  Semi-supervised K-means++.  Journal of Statistical Computation and Simulation.  87(12).  2597-2608.
  • Tang M, Priebe C (2017).  Limit theorems for eigenvectors of the normalized Laplacian for random graphs.  Annals of Statistics.
  • Shen C, Vogelstein JT, Priebe CE (2017).  Manifold matching using shortest-path distance and joint neighborhood selection.  Pattern Recognition Letters.  92.  41-48.
  • Cape J, Tang M, Priebe C (2017).  The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics.
  • Rubin-Delanchy P, Priebe C, Tang M (2017).  Consistency of adjacency spectral embedding for the mixed membership stochastic blockmodel.
  • Priebe C, Park Y, Athreya A, Lyzinski V, Vogelstein J, Qin Y, Cocanougher B, Eichler K, Zlatic M, Cardona A (2017).  Semiparametric spectral modeling of the Drosophila connectome.
  • Zheng D, Mhembere D, Lyzinski V, Vogelstein JT, Priebe CE, Burns R (2017).  Semi-external memory sparse matrix multiplication for billion-node graphs.  IEEE Transactions on Parallel and Distributed Systems.  28(5).  1470-1483.
  • Fishkind D, Adali S, Patsolic H, Meng L, Priebe C, Lyzinski V (2017).  Seeded graph matching.
  • Tang M, Athreya A, Sussman DL, Lyzinski V, Park Y, Priebe CE (2017).  A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs.  Journal of Computational and Graphical Statistics.  26(2).  344-354.
  • Shen C, Priebe C, Maggioni M, Vogelstein J (2017).  Joint embedding of graphs.
  • Maugis P-A. G, Priebe C, Olhede S.C, Wolfe P.J (2017).  Statistical inference for network samples using subgraph counts.
  • Cape J, Tang M, Priebe CE (2017).  The kato-temple inequality and eigenvalue concentration with applications to graph inference.  Electronic Journal of Statistics.  11(2).  3954-3978.
  • Lyzinski V, Tang M, Athreya A, Park Y, Priebe CE (2017).  Community Detection and Classification in Hierarchical Stochastic Blockmodels.  IEEE Transactions on Network Science and Engineering.  4(1).  13-26.
  • Lee NH, Tang R, Priebe CE, Rosen M (2016).  A Model Selection Approach for Clustering a Multinomial Sequence with Non-Negative Factorization.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(12).  2345-2358.
  • Adali S, Priebe CE (2016).  Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities.  Journal of Classification.  33(3).  485-506.
  • Priebe C, Adali S (2016).  Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities.  Journal of Classification.  33(3).  485-506.
  • Lyzinski V, Levin K, Fishkind DE, Priebe CE (2016).  On the consistency of the likelihood maximization vertex nomination scheme: Bridging the gap between maximum likelihood estimation and graph matching.  Journal of Machine Learning Research.  17.
  • Fishkind DE, Shen C, Park Y, Priebe CE (2016).  On the Incommensurability Phenomenon.  Journal of Classification.  33(2).  185-209.
  • Megarry WP, Cooney G, Comer DC, Priebe CE (2016).  Posterior probability modeling and image classification for archaeological site prospection: Building a survey efficacy model for identifying Neolithic felsite workshops in the Shetland Islands.  Remote Sensing.  8(6).
  • Chen L, Shen C, Vogelstein JT, Priebe CE (2016).  Robust Vertex Classification.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(3).  578-590.
  • Priebe C, L. Chen , C. Shen , J. V. Vogelstein (2016).  Robust Vertex Classification.  38(3).  579-590.
  • Athreya A, Priebe CE, Tang M, Lyzinski V, Marchette DJ, Sussman DL (2016).  A Limit Theorem for Scaled Eigenvectors of Random Dot Product Graphs.  Sankhya A.  78(1).
  • Priebe C, Fishkind D, Shen C, Park Y (2016).  On the Incommensurability Phenomenon.  Journal of Classification.  33(2).  185-209.
  • Priebe C, Lee N, Tang R, Rosen M (2016).  A Model Selection Approach for Clustering a Multinomial Sequence with Non-Negative Factorization.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(12).  2345-2358.
  • Priebe C, Megarry W, Cooney C, D. C (2016).  Posterior Probability Modelling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands.  Remote Sensing.  8(529).  1-18.
  • Lyzinski V, Fishkind DE, Fiori M, Vogelstein JT, Priebe CE, Sapiro G (2016).  Graph Matching: Relax at Your Own Risk.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  38(1).  60-73.
  • Suwan S, Lee DS, Tang R, Sussman DL, Tang M, Priebe CE (2016).  Empirical Bayes estimation for the stochastic blockmodel.  Electronic Journal of Statistics.  10(1).  761-782.
  • Priebe C, Lyzinksi V, Levin K, Fishkind D (2016).  On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Likelihood Estimation and Graph Matching.  17(179).  1-34.
  • Priebe C, Suwan S, Lee DS, Tang R, Sussman DL, Tang M (2016).  Empirical Bayes Estimation for the Stochastic Blockmodel.  Electronic Journal of Statistics.  10(1).  761-782.
  • Athreya A, Priebe CE, Tang M, Lyzinski V, Marchette DJ, Sussman DL (2016).  A limit theorem for scaled eigenvectors of random dot product graphs.  Sankhya: The Indian Journal of Statistics.  78A.  1-18.
  • Priebe CE, Sussman DL, Tang M, Vogelstein JT (2015).  Statistical Inference on Errorfully Observed Graphs.  Journal of Computational and Graphical Statistics.  24(4).  930-953.
  • Fishkind DE, Lyzinski V, Pao H, Chen L, Priebe CE (2015).  Vertex nomination schemes for membership prediction.  Annals of Applied Statistics.  9(3).  1510-1532.
  • Gray Roncal WR, Kleissas DM, Vogelstein JT, Manavalan P, Lillaney K, Pekala M, Burns R, Vogelstein RJ, Priebe CE, Chevillet MA, Hager GD (2015).  An automated images-to-graphs framework for high resolution connectomics.  Frontiers in Neuroinformatics.  9(AUGUST).
  • Priebe C, V. Lyzinski , D. L. Sussman , H. Pao , D. E. Fishkind , Y. Park , L. Chen , J. T. Vogelstein (2015).  Spectral Clustering for Divide-and-Conquer Graph Matching.  47.  70-87.
  • Kasthuri N, Hayworth KJ, Berger DR, Schalek RL, Conchello JA, Knowles-Barley S, Lee D, Vázquez-Reina A, Kaynig V, Jones TR, Roberts M, Morgan JL, Tapia JC, Seung HS, Roncal WG, Vogelstein JT, Burns R, Sussman DL, Priebe CE, Pfister H, Lichtman JW (2015).  Saturated Reconstruction of a Volume of Neocortex.  Cell.  162(3).  648-661.
  • Priebe C, N. Kasthuri , K. J. Hayworth , D. R. Berger , R. L. Schalek , J. A. Conchello , S. Knowles-Barley , D. Lee , A. Vásquez-Réina , V. Kaynig , T. R. Jones , M. Roberts , J. L. Morgan , J. C. Tapia , H. S. Seung , W. G. Roncal , J. T. Vogelstein... (2015).  Saturated Reconstruction of a Volume of Neocortex.  162(3).  648-661.
  • Lyzinski V, Sussman DL, Fishkind DE, Pao H, Chen L, Vogelstein JT, Park Y, Priebe CE (2015).  Spectral clustering for divide-and-conquer graph matching.  Parallel Computing.  47.  70-87.
  • Vogelstein JT, Priebe CE (2015).  Shuffled Graph Classification: Theory and Connectome Applications.  Journal of Classification.  32(1).  3-20.
  • Vogelstein JT, Conroy JM, Lyzinski V, Podrazik LJ, Kratzer SG, Harley ET, Fishkind DE, Vogelstein RJ, Priebe CE (2015).  Fast Approximate Quadratic programming for graph matching.  PLoS ONE.  10(4).
  • Priebe C, S. Suwan , D. S. Lee (2015).  Bayesian Vertex Nomination Using Content and Context.  WIREs Computational Statistics.  7(6).  400-416.
  • Priebe C, M.A. Tang , A.S. Dietz , T. Yang , P.J. Pronovost (2015).  An Integrative Framework for Sensor-based Measurement of Teamwork in Healthcare.  22(1).  11-18.
  • Alkaya AF, Aksakalli V, Priebe C (2015).  A penalty search algorithm for the obstacle neutralization problem..  Computers & Operations Research.  53.  165-175.
  • Marchette DJ, Choi SY, Rukhin A, Priebe CE (2015).  Neighborhood homogeneous labelings of graphs.  Journal of Combinatorial Mathematics and Combinatorial Computing.  93.  201-220.
  • Priebe C, J. T. Vogelstein (2015).  Shuffled Graph Classification: Theory and Connectome of Graphs.  32.  3-20.
  • Alkaya AF, Aksakalli V, Priebe CE (2015).  A penalty search algorithm for the obstacle neutralization problem.  Computers and Operations Research.  53.  165-175.
  • Priebe C, D. L. Sussman , M. Tang , J. T. Vogelstein (2015).  Statistical inference on errorfully observed graphs.  Journal of Computational and Graphical Statistics.  24(4).  930-953.
  • Priebe C, D. J. Marchette , S. Y. Choi , A. Rukhin (2015).  Neighborhood Homogeneous Labelings of Graphs.  93.  201-220.
  • Priebe C, D. E. Fishkind , V. Lyzinski , H. Pao , L. Chen (2015).  Vertex Nomination Schemes for Membership Prediction.  Annals of Applied Statistics.  9(3).  1510-1532.
  • Suwan S, Lee DS, Priebe CE (2015).  Bayesian Vertex Nomination Using Content and Context.  Wiley Interdisciplinary Reviews: Computational Statistics.  7(6).  400-416.
  • Rosen MA, Dietz AS, Yang T, Priebe C, Pronovost PJ (2015).  An integrative framework for sensor-based measurement of teamwork in healthcare..  22(1).  11-18.
  • Lyzinski V, Fishkind DE, Priebe CE (2015).  Seeded graph matching for correlated Erdo″s-Rényi graphs.  Journal of Machine Learning Research.  15.  3513-3540.
  • Priebe C, Yoder J (2016).  Semi-supervised K-means++.
  • Priebe C, Zheng D, Park Y, Lyzinski V, Vogelstein J, Burns R (2016).  Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs.  IEEE Transactions on Parallel and Distributed Systems.
  • Priebe C, Lyzinski V, Sussman D, Athreya D, Park Y (2016).  Community Detection and Classification in Hierarchical Stochastic Blockmodels.  IEEE Transactions on Network Science and Engineering.
  • Priebe C, Fishkind D, C. Shen , Y. Park (2015).  On the Incommensurability Phenomenon.  Journal of Classification.
  • Priebe C, Shen C (2015).  Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection.
  • Priebe C, S. Adali (2015).  Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities.  Journal of Classification.  33(3).  485-506.
  • Priebe C, Athreya D, V. Lyzinski , D. J. Marchette , D. L. Sussman , M. Tang (2016).  A limit theorem for scaled eigenvectors of random dot product graphs.  Sankhya.  78-A(1).  1-18.
  • Priebe C, Tang M, Athreya D, D. L. Sussman , V. Lyzinski (2015).  A Nonparametric Two-Sample Hypothesis Testing for Random Dot Product Graphs.  Bernoulli Journal.
  • Priebe C, V. Lyzinski , Fishkind D, M. Fiori , Vogelstein J, G. Sapiro (2015).  Graph Matching: Relax at Your Own Risk.
  • Priebe C (2015).  A model selection approach for clustering a multinomial sequence with non-negative factorization.
  • Trosset MW, Gao M (2016).  On the Power of Likelihood Ratio Tests in Dimension-Restricted Submodels.
  • Priebe C, Tang M, Athreya D, Sussman D, Lyzinski V, Park Y (2016).  A Semiparametric Two-Sample Hypothesis Testing for Random Dot Product Graphs.  Journal of Computational and Graphical Statistics.
  • Priebe C, L. Chen , J. T. Vogelstein , V. Lyzinski (2016).  A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Cennectomes.  Worm.  5(2).  1.
  • Priebe C, Tang R, Ketcha M, Vogelstein J, Sussman DL (2016).  Law of Large Graphs.
  • Priebe C, Eichler K, Li F, Park Y, Andrade I, Schneider-Mizell C, Huser A, Saumweber T, Huser A, Bonnery D, Gerber B, Fetter RD, Truman JW, Abbott LF, Thum A, Zlatic M, Cardona A (2016).  The Complete Wiring Diagram of a High-Order Learning and Memory Center, the Insect Mushroom Body.  Nature.
  • Priebe C, J. T. Vogelstein , J. M. Conroy , V. Lyzinski , L. J. Podrazik , S. G. Kratzer , E. T. Harley , D. E. Fishkind , R. J. Vogelstein (2015).  Fast Approximate Quadradic Programming for Large (Brain) Graph Matching.
  • Priebe C, W. G. Roncal , D. M. Kleissas , J. T. Vogelstein , R. Burns , P. Manavalan , R. J. Vogelstein , M. A. Chevillet , Hager G (2015).  An Automated Images-To-Graphs Pipeline For High Resolution Connectomics.
  • Priebe C, Shen C, Chen L (2015).  Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption.
  • Priebe C, Lyzinski V, Park Y, Trosset MW (2016).  Fast Embedding for JOFC Using the Raw Stress Criterion.  Journal of Computational and Graphical Statistics.
  • Priebe C, Qin Y (2016).  Robust Hypothesis Testing via Lq-Likelihood.  Statistica Sinica.
  • Priebe C, Tang M (2016).  Limit Theorems for Eigenvectors of the Normalized Laplacian for Random Graphs.
  • Priebe C, Yoder J (2015).  A Model-Based Semi-Supervised Clustering Methodology.
  • Priebe C, Athreya D, Tang M, Lyzinski V, Park Y, Lewis B, Kane M (2016).  Numerical Tolerance for Spectral Decompositions of Random Dot Product Graphs.
Conference Proceedings
  • Priebe C, Campbell WM, Li L, Acevedo-Aviles J, Dagli C, Campbell JP, Greyer K (2016).  Cross-Domain Entity Resolution in Social Media.  The 4th International Workshop on Natural Language Processing for Social Media.
  • Priebe C, Zheng D, Burns R, Vogelstein J, Szakay AS (2016).  An SSD-based Eigensolver for Spectral Analysis on Billion-Node Graph.  CoRR.
  • Priebe C (2015).  Learning Statistical Manifolds for Subsequent Inference: A Duet.  Indiana University.
  • Priebe C (2015).  Community Detection and Classification in Hierarchical Stochastic Blockmodels.  Dartmouth College.
  • Priebe C, Park Y, Wang H, No¨bauer T (2015).  Anomaly Detection on Whole-Brain Functional Imaging of Neuronal Activity using Graph Scan Statistics.  21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Workshop on Outlier Definition, Detection, and Description.
  • Priebe C (2015).  Big Data & Statistics at University of Costa Rica.
  • Priebe C, Wang H, Zheng D, Burns R (2015).  Active Community Detection in Massive Graphs.  SDM- Networks 2015: The Second SDM Workshop on Mining Networks and Graphs: A Big Data Analytic Challenge.
  • Priebe C (2015).  Community Detection and Classification in Hierarchical Stochastic Blockmodels.  Statistical and Computational Challenges in Networks and Cybersecurity.
  • Priebe C, Vogelstein J, Bogovic J, Carass A, Gray WR, Prince JL, Bennett L, Ferrucci L, Resnick SM, Vogelstein RJ (2015).  Graph-Theoretical Methods for Statistical Inference on MR Connectome Data.  OHBM.
  • Priebe C, Zheng D, Mhembere D, Burns R, Vogelstein J, Szalay AS (2015).  FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs.  In 13th USENIX Conference on File and Storage Technologies (FAST 15).
  • "Limit Theorems for Eigenvectors of the Normalized Laplacian for Random Graphs", Theoretical Foundations for Statistical Network Analysis.  Cambridge, England.  October 6, 2016
  • "Semiparametric Spectral Modeling of the Drosophila Connectome", Theoretical Foundations for Statistical Network Analysis.  Cambridge, England.  August 22, 2016
  • "Repeated Motif Hierarchical Stochastic Blockmodels", Graph Limits and Statistics.  Cambridge, England.  July 15, 2016
  • "Community Detection and Classification in Hierarchical Stochastic Blockmodels".  England.  May 6, 2016
  • "Community Detection and Classification in Hierarchical Stochastic Blockmodels".  London, England.  April 29, 2016
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