Vince Lyzinski

2306 William E. Kirwan Hall
4176 Campus Dr
College Park, MD 20742

email: vlyzinsk (at sign) umd (dot) edu
[arXiv ID]
[Google Scholar]

Associate Professor, Statistics Program, Department of Mathematics
University of Maryland, College Park

Additional affiliations
Applied Mathematics, Statistics, and Scientific Computation (AMSC) program

Research interests

Statistical network inference

Graph matching (theory and algorithms)

Statistical machine learning

Markov chains

Probability

Combinatorics

Current and/or past research generously supported by DARPA, NIH and JHU HLTCOE

An overview of our work on the DARPA MAA program can be found here.

My c.v. can be found here.
My dissertation can be found here.

Education

Ph.D. in Applied Mathematics and Statistics, Johns Hopkins University, 2013
Advisor: Prof. James Allen Fill
Dissertation title: Intertwinings, Interlacing Eigenvalues, and Strong Stationary Duality for Diffusions

M.S.E. in Applied Mathematics and Statistics, Johns Hopkins University, 2011

M.A. in Mathematics, Johns Hopkins University, 2007

B.S. in Mathematics, University of Notre Dame 2006
Budapest Semesters in Mathematics, Spring 2005

Current Ph.D. Advisees

Al-Fahad Al-Qadhi (UMD AMSC)
Zhirui Li (UMD AMSC)
Tong Qi (UMD STAT)

Past Ph.D. Advisees (and those about to finish)

Sheyda Peyman (UMD AMSC)
Thesis title: Structured Discovery in Graphs: Recommender Systems and Temporal Graph Analysis
Graduated May 2024
Ayushi Saxena (UMD STAT)
Thesis title: The Shuffling Effect: Vertex Label Error's Impact on Hypothesis Testing, Classification, and Clustering in Graph Data
Graduated May 2024 , federal government employment after graduation
Konstantinos Pantazis (UMD MATH)
Thesis title: Statistical Inference across Multiple Graphs: Advancements in Multiplex Graph Matching and Joint Spectral Graph Embeddings
Graduated 2022, internship at MSR then PostDoc at JHU in Applied Math and Stats
Jesse Milzman (UMD MATH, co-advised with Prof. Doron Levy)
Thesis title: Dynamics, Networks, and Information: Methods for Nonlinear Interactions in Biological Systems
Graduated 2021, now at Army Research Lab
Heather Gaddy Patsolic (JHU AMS, co-advised with Prof. Carey Priebe)
Dissertation Title: Graph Matching and Vertex Nomination
Graduated 2020, now at Accenture Federal Services
Keith Levin (JHU CS, co-advised with Profs. Ben Van Durme and Carey Priebe)
Thesis title: Graph Inference with Applications to Low-Resource Audio Search and Indexing
Graduated 2017, now an Assistant Professor (tenure-track) in the Department of Statistics at University of Wisconsin

Past Postdoc Advisees

Jesus Arroyo (UMD MATH Postdoc, AY 2020-2021)
Now an Assistant Professor (tenure-track) in the Department of Statistics at Texas A&M University

My refereed journal publications can be (roughly) split into five main areas

Graph matching/network de-anonymization

Vertex nomination/IR on networks

Statistical network inference (clustering, testing, classification, etc.)

Markov chains/processes

Misc.

For refereed conference/workshop publications, click here

Gotta match 'em all: Solution diversification in graph matching matched filters
Z. Li, B. Johnson, D.L. Sussman, C.E. Priebe, V. Lyzinski
IEEE Transactions on Signal and Information Processing over Networks (accepted for publication 2024+)
Link, code implementing the modified MGMMF algorithm can be found here
Clustered Graph Matching for Label Recovery and Graph Classification
Z. Li, J. Arroyo, K.Pantazis, V. Lyzinski
IEEE Transactions on Network Science and Engineering, 10, no. 6:3384-3395 (2023)
Link
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
A.-F. M. Al-Qadhi, C. E. Priebe, H. S. Helm, and V. Lyzinski
Statistics and Computing, 33, no. 2 (2023)
Link
Numerical Tolerance for Spectral Decompositions of Random Matrices and Applications to Network Inference
A. Athreya, Z. Lubberts, C. E. Priebe, Y. Park, M. Tang, V. Lyzinski, M. Kane, B. W. Lewis
Journal of Computational and Graphical Statistics, 32:1, 145-156, (2023)
Link
Signed and Unsigned Partial Information Decompositions of Continuous Network Interactions
J. Milzman, V. Lyzinski
Journal of Complex Networks, Volume 10, Issue 5, October 2022, cnac026, https://doi.org/10.1093/comnet/cnac026 (2022)
Link
Multiplex graph matching matched filters
K. Pantazis, D. L. Sussman, Y. Park, Z. Li C. E. Priebe, V. Lyzinski
Applied Network Science, 7, Article number 29 (2022)
Link
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
K. Pantazis, A. Athreya, J. Arroyo, W. N. Frost, E. S. Hill, V. Lyzinski
Journal of Machine Learning Research, Vol. 23(141):1-77, (2022)
Link
Vertex nomination between graphs via spectral embedding and quadratic programming
R. Zheng, V. Lyzinski, C. E. Priebe, and M. Tang
Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2022.2060238 (2022)
Link
The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match (previous title was "On Phantom Alignment Strength")
D. E. Fishkind, F. Parker, H. Sawczuk, L. Meng, E. Bridgeford, A. Athreya, C. E. Priebe, and V. Lyzinski
Applied Network Science, 6, Article number: 62 (2021)
Link
Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
J. Arroyo, C. E. Priebe, V. Lyzinski
IEEE Transactions on Network Science and Engineering, 8.4 (2021): 3019-3033
Link, Code
On a complete and sufficient statistic for the correlated Bernoulli random graph model
D. E. Fishkind, A. Athreya, L. Meng, V. Lyzinski, C. E. Priebe
Electronic Journal of Statistics, (2021) 15.1: 2336-2359
Link
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
J. Arroyo, D. L. Sussman, C. E. Priebe, V. Lyzinski
Journal of Computational and Graphical Statistics, (2021), pp. 1-13, doi 10.1080/10618600.2021.1872582
Link, Code
Vertex Nomination, Consistent Estimation, and Adversarial Modification
J. Agterberg, Y. Park, J. Larson, C. White, C. E. Priebe, V. Lyzinski
Electronic Journal of Statistics (2020), vol. 14.2, pp. 3230-3267
Link
Vertex Nomination Via Seeded Graph Matching
H. G. Patsolic, Y. Park, V. Lyzinski , C. E. Priebe
Statistical Analysis and Data Mining (2020), vol. 13.3, pp. 229-244
Link
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
J. Yoder, L. Chen, H. Pao, E. Bridgeford, K. Levin, D. E. Fishkind, C. E. Priebe, V. Lyzinski
Computational Statistics & Data Analysis (2020), vol. 145
Link
Matchability of heterogeneous networks pairs
V. Lyzinski , D. L. Sussman
Information and Inference: a Journal of the IMA 9.4 (2020): 749-783
Link
Matched filters for noisy induced subgraph detection
D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
IEEE Transcations on Pattern Analysis and Machine Intelligence vol. 42, no. 11, pp. 2887-2900, 1 Nov. 2020
Link
On Consistent Vertex Nomination Schemes
V. Lyzinski, K. Levin, C.E. Priebe
Journal of Machine Learning Research (2019), no. 69, pp. 1-39
Link
Neural Variational Entity Set Expansion for Automatically Populated Knowledge Graphs
P. Rastogi, A. Poliak, V. Lyzinski, B. Van Durme
Information Retrieval Journal (2019), 22.3-4, pp. 232-255
[abstract can be found. here]
On a 'Two-Truths' Phenomenon in Spectral Graph Clustering
C. E. Priebe, Y. Park, J. T. Vogelstein, J. M . Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, E. Bridgeford
Proceedings of the National Academy of Sciences (2019), 116.13, pp. 5995-6000
Link
Alignment strength and correlation for graphs
D. E. Fishkind, L. Meng, A. Sun, C. E. Priebe, V. Lyzinski
Pattern Recognition Letters (2019), 125, pp. 203--215
Link
Seeded Graph Matching
D. E. Fishkind, S. Adali, H. G. Patsolic, L. Meng, D. Singh, V. Lyzinski, C.E. Priebe
Pattern Recognition (2019), Vol. 87, pp. 203--215
Link
Statistical Inference on Random Dot Product Graphs: A Survey
A. Athreya, D.E. Fishkind, K. Levin, V. Lyzinski, Y. Park, Y. Qin, D.L. Sussman, M. Tang, J.T. Vogelstein, C.E. and Priebe
Journal of Machine Learning Research (2018), Vol. 18, pp. 1-92
Link
Information Recovery in Shuffled Graphs via Graph Matching
V. Lyzinski
IEEE Transactions on Information Theory (2018), 64(5), pp.3254-3273
Link
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
A. Jansen, G. Sell, V. Lyzinski
Pattern Recognition Letters (2017), Vol. 94(15), pp. 1-6
Link
Fast Embedding for JOFC Using the Raw Stress Criterion
V. Lyzinski, Y. Park, C. E. Priebe and Michael Trosset
Journal of Computational and Graphical Statistics (2017), 26(4), pp. 786-802
Link
A semiparametric two-sample hypothesis testing problem for random dot product graphs
M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
Journal of Computational and Graphical Statistics (2017), 26(2), 344-354
Link
A nonparametric two-sample hypothesis testing problem for random dot product graphs
M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
Bernoulli Journal (2017), Vol. 23 no. 3, 1599-1630
Link
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
K. Levin and V. Lyzinski
IEEE Transactions on Signal Processing (2017), Vol. 65(8)
Link
Community Detection and Classification in Hierarchical Stochastic Blockmodels
V. Lyzinski, M. Tang, A. Athreya, Y. Park and C. E. Priebe
IEEE Transactions on Network Science and Engineering (2017), 4(1), pp.13-26
Link
Data, code and figures are presented in more detail here
Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture
D. Zheng, D. Mhembere, V. Lyzinski, J. Vogelstein, C. E. Priebe, and R. Burns
IEEE Transactions in Parallel and Distributed Systems (2017), Vol. 28(5), pp 1470-1483
Link
A limit theorem for scaled eigenvectors of random dot product graphs
A. Athreya, C. E. Priebe, M. Tang, V. Lyzinski, D. J. Marchette and D. L. Sussman
Sankhya A (2016), 78.1, pp 1-18
Link
A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes
L. Chen, J. T. Vogelstein, V. Lyzinski, C. E. Priebe
Worm (2016), 5(2)
Link
Graph Matching: Relax at Your Own Risk
V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe and G. Sapiro
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016), 38(1), pp.60-73
Link
Strong Stationary Duality for Diffusion Processes
J. A. Fill and V. Lyzinski
Journal of Theoretical Probability (2016), Vol. 29(4), pp. 1298-1338
Link
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
V. Lyzinski, K. Levin, D. E. Fishkind and C. E. Priebe
Journal of Machine Learning Research (2016), 17(179), pp.1-34
Link
Vertex Nomination Schemes for Membership Prediction
D. E. Fishkind, V. Lyzinski, H. Pao, L. Chen and C. E. Priebe
Annals of Applied Statistics (2015), 9.3 pp 1510-1532
Link
Logarithmic representability of integers as k-sums
A. Godbole, S. Gutekunst, V. Lyzinski and Y. Zhuang
Integers: The Electronic Journal of Combinatorial Number Theory (2015), vol. 15A: Proceedings of Integers 2013: The Erdős Centennial Conference
Link
Fast Approximate Quadratic Programming for Graph Matching
J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, C. E. Priebe
PLOS One (2015), 10(4): e0121002. doi:10.1371/journal.pone.0121002
Link
Spectral Clustering for Divide-and-Conquer Graph Matching
V. Lyzinski, D. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. Vogelstein, Y. Park and C. E. Priebe
Parallel Computing (2015), doi:10.1016/j.parco.2015.03.004
Link
Perfect clustering for stochastic block model graphs via adjacency spectral embedding
V. Lyzinski, D. Sussman, M. Tang, A. Athreya and C. E. Priebe
Electronic Journal of Statistics (2014), 8, pp 2905-2922
Link
Hitting times and interlacing eigenvalues: a stochastic approach using intertwinings
J. A. Fill and V. Lyzinski
Journal of Theoretical Probability (2014), Vol. 27(3), pp. 954-981
Link
Seeded graph matching for correlated Erdos-Renyi graphs
V. Lyzinski, D. E. Fishkind and C. E. Priebe
Journal of Machine Learning Research (2014), 15, pp 3513-3540
Link
Sharp threshold asymptotics for the emergence of additive bases
A. Godbole, C. M. Lim, V. Lyzinski and N. Triantafillou
Integers: The Electronic Journal of Combinatorial Number Theory (2013), vol. 13
Link

Refereed conference/workshop publications

Multiplex graph matching matched filters
K. Pantazis, D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
GTA3 3.0: The 3rd workshop on Graph Techniques for Adversarial Activity Analytics, in Conjunction with the 2019 IEEE Big Data Conference, Los Angeles, CA, Dec 9, 2019
Matched Filters for Noisy Induced Subgraph Detection
D. Sussman, V. Lyzinski, Y. Park, C. E. Priebe
GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 9, 2018
(won the best paper award at the workshop)
Link
Metrics for Evaluating Network Alignment
J. Douglas, B. Zimmerman, A. Kopylov, J. Xu, D. Sussman, V. Lyzinski
GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 9, 2018
Link
A Central Limit Theorem for an Omnibus Embedding of Multiple Random Dot Product Graphs
K. Levin, A. Avanti, M. Tang, V. Lyzinski, C. E. Priebe
2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 964-967. IEEE
Link
An Evaluation of Graph Clustering Methods for Unsupervised Term Discovery
V. Lyzinski, G. Sell, and A. Jansen
Proceedings of Interspeech (2015), Dresden, Germany
Link

Publications

Gotta match 'em all: Solution diversification in graph matching matched filters
Z. Li, B. Johnson, D.L. Sussman, C.E. Priebe, V. Lyzinski
IEEE Transactions on Signal and Information Processing over Networks (accepted for publication 2024+)
Link, code implementing the modified MGMMF algorithm can be found here

Clustered Graph Matching for Label Recovery and Graph Classification
Z. Li, J. Arroyo, K.Pantazis, V. Lyzinski
IEEE Transactions on Network Science and Engineering, 10, no. 6:3384-3395 (2023)
Link

Multiplex graph matching matched filters
K. Pantazis, D. L. Sussman, Y. Park, Z. Li C. E. Priebe, V. Lyzinski
Applied Network Science, 7, Article number 29 (2022)
Link

The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match (previous title was "On Phantom Alignment Strength")
D. E. Fishkind, F. Parker, H. Sawczuk, L. Meng, E. Bridgeford, A. Athreya, C. E. Priebe, and V. Lyzinski
Applied Network Science, 6, Article number: 62 (2021)
Link

Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
J. Arroyo, C. E. Priebe, V. Lyzinski
IEEE Transactions on Network Science and Engineering, 8.4 (2021): 3019-3033
Link, Code

On a complete and sufficient statistic for the correlated Bernoulli random graph model
D. E. Fishkind, A. Athreya, L. Meng, V. Lyzinski, C. E. Priebe
Electronic Journal of Statistics, (2021) 15.1: 2336-2359
Link

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
J. Arroyo, D. L. Sussman, C. E. Priebe, V. Lyzinski
Journal of Computational and Graphical Statistics, (2021), pp. 1-13, doi 10.1080/10618600.2021.1872582
Link, Code

Matchability of heterogeneous networks pairs
V. Lyzinski , D. L. Sussman
Information and Inference: a Journal of the IMA 9.4 (2020): 749-783
Link

Matched filters for noisy induced subgraph detection
D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
IEEE Transcations on Pattern Analysis and Machine Intelligence vol. 42, no. 11, pp. 2887-2900, 1 Nov. 2020
Link

Alignment strength and correlation for graphs
D. E. Fishkind, L. Meng, A. Sun, C. E. Priebe, V. Lyzinski
Pattern Recognition Letters (2019), 125, pp. 203--215
Link

Seeded Graph Matching
D. E. Fishkind, S. Adali, H. G. Patsolic, L. Meng, D. Singh, V. Lyzinski, C.E. Priebe
Pattern Recognition (2019), Vol. 87, pp. 203--215
Link

Information Recovery in Shuffled Graphs via Graph Matching
V. Lyzinski
IEEE Transactions on Information Theory (2018), 64(5), pp.3254-3273
Link

Graph Matching: Relax at Your Own Risk
V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe and G. Sapiro
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016), 38(1), pp.60-73
Link

Fast Approximate Quadratic Programming for Graph Matching
J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, C. E. Priebe
PLOS One (2015), 10(4): e0121002. doi:10.1371/journal.pone.0121002
Link

Spectral Clustering for Divide-and-Conquer Graph Matching
V. Lyzinski, D. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. Vogelstein, Y. Park and C. E. Priebe
Parallel Computing (2015), doi:10.1016/j.parco.2015.03.004
Link

Seeded graph matching for correlated Erdos-Renyi graphs
V. Lyzinski, D. E. Fishkind and C. E. Priebe
Journal of Machine Learning Research (2014), 15, pp 3513-3540
Link

Publications

Numerical Tolerance for Spectral Decompositions of Random Matrices and Applications to Network Inference
A. Athreya, Z. Lubberts, C. E. Priebe, Y. Park, M. Tang, V. Lyzinski, M. Kane, B. W. Lewis
Journal of Computational and Graphical Statistics, 32:1, 145-156, (2023)
Link

The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
K. Pantazis, A. Athreya, J. Arroyo, W. N. Frost, E. S. Hill, V. Lyzinski
Journal of Machine Learning Research Vol. 23(141):1-77, (2022)
Link

On a 'Two-Truths' Phenomenon in Spectral Graph Clustering
C. E. Priebe, Y. Park, J. T. Vogelstein, J. M . Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, E. Bridgeford
Proceedings of the National Academy of Sciences (2019), 116.13, pp. 5995-6000
Link

Statistical Inference on Random Dot Product Graphs: A Survey
A. Athreya, D.E. Fishkind, K. Levin, V. Lyzinski, Y. Park, Y. Qin, D.L. Sussman, M. Tang, J.T. Vogelstein, C.E. and Priebe
Journal of Machine Learning Research (2018), Vol. 18, pp. 1-92
Link

Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
A. Jansen, G. Sell, V. Lyzinski
Pattern Recognition Letters (2017), Vol. 94(15), pp. 1-6
Link

Fast Embedding for JOFC Using the Raw Stress Criterion
V. Lyzinski, Y. Park, C. E. Priebe and Michael Trosset
Journal of Computational and Graphical Statistics (2017), 26(4), pp. 786-802
Link

A semiparametric two-sample hypothesis testing problem for random dot product graphs
M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
Journal of Computational and Graphical Statistics (2017), 26(2), 344-354
Link

A nonparametric two-sample hypothesis testing problem for random dot product graphs
M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
Bernoulli Journal (2017), Vol. 23 no. 3, 1599-1630
Link

Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
K. Levin and V. Lyzinski
IEEE Transactions on Signal Processing (2017), Vol. 65(8)
Link

Community Detection and Classification in Hierarchical Stochastic Blockmodels
V. Lyzinski, M. Tang, A. Athreya, Y. Park and C. E. Priebe
IEEE Transactions on Network Science and Engineering (2017), 4(1), pp.13-26
Link
Data, code and figures are presented in more detail here

A limit theorem for scaled eigenvectors of random dot product graphs
A. Athreya, C. E. Priebe, M. Tang, V. Lyzinski, D. J. Marchette and D. L. Sussman
Sankhya A (2016), 78.1, pp 1-18
Link

A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes
L. Chen, J. T. Vogelstein, V. Lyzinski, C. E. Priebe
Worm (2016), 5(2)
Link

Perfect clustering for stochastic block model graphs via adjacency spectral embedding
V. Lyzinski, D. Sussman, M. Tang, A. Athreya and C. E. Priebe
Electronic Journal of Statistics (2014), 8, pp 2905-2922
Link

Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings
K. Pantazis, M. Trosset, W. N. Frost, C. E. Priebe, V. Lyzinski
arXiv 2409.17544 (2024)
Link
Code implementing experiments can be found here

ACRONYM: Augmented degree corrected, Community Reticulated Organized Network Yielding Model
B. Leinwand, V. Lyzinski
arXiv 2404.07462 (2024)
Link

Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embedding
T. Qi, V. Lyzinski
arXiv 2312.11054 (2023)
Link, Code implementing experiments can be found here

Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression
T. Lu, Y. Zhang, V. Lyzinski, C. Bi, P. Kochunov, E. Hong, S. Chen
arXiv 2310.18533 (2023)
Link, code implementing MOAT can be found here

On seeded subgraph-to-subgraph matching: The ssSGM Algorithm and matchability information theory
L. Meng, M. Lou, J. Lin, V. Lyzinski, D.E. Fishkind
arXiv 2306.04016 (2023)
Link

Adversarial contamination of networks in the setting of vertex nomination: a new trimming method
S. Peyman, M. Tang, V. Lyzinski
arXiv 2208.09710 (2022)
Link

Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels
A. Saxena, V. Lyzinski
arXiv 2208.08638 (2022)
Link, Code for replicating the experiments can be found here

Leveraging semantically similar queries for ranking via combining representations
H. S. Helm, M. Abdin, B. D. Pedigo, S. Mahajan, V. Lyzinski, Y. Park, A. Basu, C. M. White, W. Yang, and C. E. Priebe
arXiv 2106.12621 (2021)
Link

On the role of features in vertex nomination: Content and context together are better (sometimes)
K. Levin, C. E. Priebe, V. Lyzinski
arXiv 2005.02151 (2020)
Link

Tractable Graph Matching via Soft Seeding
F. Fang, D. L. Sussman, V. Lyzinski
arXiv 1807.09299 (2018)
Link

Semiparametric spectral modeling of the Drosophila connectome
C. E. Priebe, Y. Park, M. Tang, A. Athreya, V. Lyzinski, J. T. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, A. Cardona
arXiv 1705.03297 (2017)
Link

A central limit theorem for an omnibus embedding of random dot product graphs
K. Levin, A. Athreya, M. Tang, V. Lyzinski, C.E. Priebe
arXiv 1705.09355, (2017)
Link

Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
H. Patsolic, S. Adali, J. Vogelstein, Y. Park, C. E. Priebe, G. Li, V. Lyzinski
arXiv 1401.3813 (2014, revision submitted 2019)
Link

R code packages implementing our work can be downloaded from github at the following links.

iGraphMatch: Tools to find the correspondences between vertices in different graphs. This package implements our graph matching code base (and numerous other graph matching algorithms from the literature). Work and maintenance are funded (in part) by my DARPA MAA awards (award numbers FA8750-20-2-1001 and FA8750-18-0035). Earlier versions of our graph matching code can be found here, and in the igraph R package (the match_vertices function)

iGraphMatch

CRAN

VN: Implements the algorithms from
Vertex Nomination Via Seeded Graph Matching
H. G. Patsolic, Y. Park, V. Lyzinski , C. E. Priebe
Statistical Analysis and Data Mining (2020), vol. 13.3, pp. 229-244
Code and worked out examples can be found here

HTML web pages with downloadable code and worked examples can be found at the following links.

Seeded Graph Matching

On a 'Two Truths' Phenomenon in Spectral Graph Clustering

Community Detection and Classification in Hierarchical Stochastic Blockmodels

Fast Embedding for JOFC Using the Raw Stress Criterion
(supplemental code also available at publisher's site here)

Vertex Nomination Schemes for Membership Prediction

Numerical tolerance for spectral decompositions of random matrices (preprint)

Semiparametric spectral modeling of the Drosophila connectome (preprint)
(code reproduces figures also found in our JMLR survey paper)

Fall 2024

STAT689 (RIT): High-Dimensional Statistics
Description: An RIT dedicated to some of the modern developments in high dimensional statistical inference (following closely the textbook: High-Dimensional Statistics: A Non-asymptotic Viewpoint, by Martin Wainwright, Cambridge Univ. Press 2019)
Meetings: W 12:00pm - 12:50pm Mth 0201

STAT705: Computational Statistics in R
Official description (a bit outdated!): Modern methods of computational statistics and their application to both practical problems and research. S-Plus and SAS programming with emphasis on S-Plus. S-Plus objects and functions, and SAS procedures. Topics include data management and graphics, Monte Carlo and simulation, bootstrapping, numerical optimization in statistics, linear and generalized linear models, nonparametric regression, time series analysis. (My blurb: Statistical research and application has changed dramatically because of cheap and powerful computational and graphical tools. This course presents modern methods of computational statistics and their application to both practical problems and research. The techniques covered in STAT 705, which include some numerical-analysis ideas arising particularly in Statistics, should be part of every statistician's toolbox.)
Lectures: TTh 11:00am - 12:15pm EGR 1102
Textbooks:
- Givens, Geof H., and Jennifer A. Hoeting. Computational statistics. Vol. 703. John Wiley & Sons, 2012. This text is available for free online through the UMD library website
- Wickham, Hadley, Mine Cetinkaya-Rundel, and Garrett Grolemund. R for data science. " O'Reilly Media, Inc.", 2023. This book is available for free online here
- James, Gareth. "An introduction to statistical learning." (2nd edition 2023), by G James, D Witten, T Hastie, R Tibshirani. This book is available for free online here
Course information will be available on the course ELMS/Canvas website

DATA607: Communication in Data Science and Analytics
Description: Expected learning outcomes include that, in the context of data science and analytics, students should be able to: summarize, report, organize prose, statistics, graphics, and presentations; explain uncertainty, sensitivity/robustness, limitations; describe model generation and representation; discuss interpretations and implications; communicate effectively to diverse audiences within a business organization, and possibly other outcomes.
Lectures: W 6:00pm - 8:45pm EGR 1108 (Must be in Graduate Programs in Science Academy. For permission to enroll, contact oes@umd.edu)
Textbooks: In addition to course handouts and online resources, readings for the course will be assigned from the following textbooks:
- Healy, Kieran. Data visualization: a practical introduction. Princeton University Press, 2018. (Note that an (incomplete) online version is available here)
- Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. R for data science. O'Reilly Media, Inc., 2023. This book is available for free online at here
- Nolan, Deborah, and Sara Stoudt. Communicating with data: The art of writing for data science. Oxford University Press, 2021. (This book is available for free from the UMD library website).
- The Elements of Style, by William Strunk and E. B. White.
- The Visual Display of Quantitative Information, by Edward Tufte (2001).
Further course information will be available on the course ELMS/Canvas website

Past Semesters (at UMD)

Spring 2024: DATA350, STAT650, STAT689 (RIT)
Fall 2023: DATA350, DATA607
Spring 2023: STAT426, STAT650
Fall 2022: STAT426, DATA607
Spring 2022: STAT426, STAT650
Fall 2021: STAT426, DATA607
Spring 2021: STAT650
Winter 2020-2021: DATA607
Fall 2020: STAT705
Spring 2020: STAT426, STAT689 (RIT)
Fall 2019: STAT426

Past Semesters (at UMASS)

Spring 2019: STAT516, STAT496 (Independent Study)
Fall 2018: STAT697S
Spring 2018: STAT516 (2 Sections), MATH596 (Independent Study)

Past Semesters (at JHU)

Fall 2016: 550.621
Fall 2015: 550.620
Spring 2014: 550.771
Fall 2013: 550.310
Summer 2013: 550.111
Spring 2013: 550.111
Fall 2012: 550.310
Summer 2012: 550.230
Fall 2011: 550.310
Summer 2011: 550.230
Spring 2011: 550.310
Spring 2010: 550.310
Summer 2009: 550.171
Summer 2008: 550.111

Vince Lyzinski

Advising

Publications

Preprints

Code

Teaching

Fall 2024

Past Semesters (at UMD)

Past Semesters (at UMASS)

Past Semesters (at JHU)