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**

My c.v. can be found here.

My dissertation can be found here.

** Education**

Advisor: Prof. James Allen Fill

Dissertation title: Intertwinings, Interlacing Eigenvalues, and Strong Stationary Duality for Diffusions

Budapest Semesters in Mathematics, Spring 2005

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

For refereed conference/workshop publications, click here

** Graph matching/network de-anonymization **

**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*, accepted for publication, (2021)

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

** Vertex nomination/IR on networks **

**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

**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 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

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

**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

** Markov chains/processes **

**Strong Stationary Duality for Diffusion Processes**

J. A. Fill and**V. Lyzinski**

*Journal of Theoretical Probability*(2016), Vol. 29(4), pp. 1298-1338

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

** Misc. **

**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

**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

**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

**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

**Subgraph nomination: Query by Example Subgraph Retrieval in Networks**

A.-F. M. Al-Qadhi, C. E. Priebe, H. S. Helm, and**V. Lyzinski**

arXiv 2101.12430 (2021)

Link

**Vertex nomination between graphs via spectral embedding and quadratic programming**

R. Zheng,**V. Lyzinski**, C. E. Priebe, and M. Tang

arXiv 2010.14622 (2020)

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**

arXiv 2008.00163 (2020)

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

**Multiplex graph matching matched filters**

K. Pantazis, D. L. Sussman, Y. Park, C. E. Priebe,**V. Lyzinski**

arXiv 1908.02572 (2019)

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

**Numerical Tolerance for Spectral Decompositions of Random Dot Product Graphs**

A. Athreya, M. Kane, B. Lewis, Z. Lubberts,**V. Lyzinski**, Y. Park, C. E. Priebe, M. Tan

arXiv 1608.00451 (2016, revision submitted 2020)

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

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)

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

Fast Embedding for JOFC Using the Raw Stress Criterion

(supplemental code also available at publisher's site here)

Semiparametric spectral modeling of the Drosophila connectome (preprint)

(code reproduces figures also found in our JMLR survey paper)

**STAT426:**Introduction to Data Science and Machine Learning

**Description:**An introductory course to the recent developments in the fields of data science and machine learning. Emphasis will be given to mathematical and statistical understanding of commonly used methods and processes.

**Lectures:**TuTh 12:30pm - 1:45pm PHY 1204

**Textbooks:**We will be using (among other texts/handouts)- The Elements of Statistical Learning (2nd edition) by Hastie, Tibshirani and Friedman (2009), Springer- Verlag. This book is available online here.
- An Introduction to Statistical Learning with Applications in R, by James, Witten, Hastie and Tibshirani (2013), Springer. This book is available online here.

Further 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 CSI 1122 and online (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, and Garrett Grolemund. R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc., 2016. This book is available online at here
- Kahneman, D., Slovic, S. P., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge university press.
- The Elements of Style, by William Strunk and E. B. White.

Further course information will be available on the course ELMS/Canvas website