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**

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

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

- Al-Fahad Al-Qadhi (UMD AMSC)
- Zhirui Li (UMD AMSC)
- Sheyda Peyman (UMD AMSC)
- Tong Qi (UMD STAT)
- Ayushi Saxena (UMD STAT)

- 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

- 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** Refereed conference/workshop publications **

For refereed conference/workshop publications, click here

**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*, accepted for publication (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**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. Tang

*Journal of Computational and Graphical Statistics,*DOI: 10.1080/10618600.2022.2082972 (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

**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

**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

**Clustered Graph Matching for Label Recovery and Graph Classification**

Z. Li, J. Arroyo, K.Pantazis,**V. Lyzinski**

arXiv 2112.12316 (2022)

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

**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

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:**T/Th 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 (2nd edition), 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:**Th 6:00pm - 8:45pm CSI 1122 (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