Keith Levin
	Assistant Professor
	Department of Statistics
	University of Wisconsin
	Madison, WI 53706
	Office: 6170 Medical Sciences Center
	1300 University Ave
	E-mail: kdlevin AT wisc DOT edu
	Phone: 608-265-4047
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Research

My research focuses on network analysis, dimension reduction, concentration inequalities and clustering problems, with applications to neuroscience and speech processing.

Teaching

Fall 2024: STAT 310: Introduction to Probability and Mathematical Statistics II
Previous courses

Education

Ph.D. Computer Science, Johns Hopkins University, 2017
Thesis: Graph Inference with Applications to Low-Resource Audio Search and Indexing, co-advised by Vince Lyzinski, Benjamin Van Durme and Carey E. Priebe.
M.S.E. Computer Science, Johns Hopkins University, 2016
B.S. Linguistics, B.S. Psychology, Northeastern University, 2011

Preprints and Manuscripts

A. Hayes, and K. Levin. Peer effects in the linear-in-means model may be inestimable even when identified. 2024.

H. Yan and K. Levin. Minimax rates for latent position estimation in the generalized random dot product graph. 2023.

A. Hayes, M. M. Fredrickson and K. Levin. Estimating network-mediated causal effects via spectral embeddings. 2023.

D. Kessler, K. Levin and E. Levina. Prediction of Network Covariates Using Edge and Node Attributes. 2022.

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

K. Levin and E. Levina. Bootstrapping Networks with Latent Space Structure. 2019. Code

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

Journal Publications

K. Levin and B. Betancourt. Fast Generation of Exchangeable Sequence of Clusters Data. Statistics and Computing. 34(2), 2024.

A. Lodhia, K. Levin and E. Levina. Matrix Means and a Novel High-Dimensional Shrinkage Phenomenon. Bernoulli, 28(4):2578-2605, 2022.

K. Levin, A. Lodhia and E. Levina. Recovering shared structure from multiple networks with unknown edge distributions. Journal of Machine Learning Research, 23(3):1-48, 2022.

K. Levin, F. Roosta, M. Tang, M. W. Mahoney and C. E. Priebe. Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. Journal of Machine Learning Research, 22(194):1-59, 2021.

V. Braverman, H. Lang, K. Levin and Y. Rudoy. Metric k-Median Clustering in Insertion-Only Streams. Discrete Applied Mathematics, 304:164-180, 2021.

C. M. Le, K. Levin, P. J. Bickel and E. Levina. Comment: Ridge Regression and Regularization of Large Matrices. Technometrics, 62(4):443-446, 2020.

J. Yoder, L. Chen, H. Pao, E. Bridgeford, K. Levin, D. E. Fishkind, C. E. Priebe and V. Lyzinski. Vertex nomination: The canonical sampling and the extended spectral nomination schemes. Computational Statistics and Data Analysis, 145:106916, 2020.

V. Lyzinski, K. Levin and C. E. Priebe. On consistent vertex nomination schemes. Journal of Machine Learning Research, 20(69):1-39, 2019.

J. T. Vogelstein, E. W. Bridgeford, B. D. Pedigo, J. Chung, K. Levin, B. Mensh and C. E. Priebe. Connectal coding: discovering the structures linking cognitive phenotypes to individual histories. Current Opinion in Neurobiology, 55:199-212, 2019.

C. M. Le, K. Levin and E. Levina. Estimating a network from multiple noisy realizations. Electronic Journal of Statistics, 12(2):4697-4740, 2018.

A. Athreya, D. E. Fishkind, K. Levin, V. Lyzinski, Y. Park, Y. Qin, D. L. Sussman, M. Tang, J. T. Vogelstein and C. E. Priebe. Statistical inference on random dot product graphs: a survey. Journal of Machine Learning Research, 18(226):1-92, 2018.

K. Levin and V. Lyzinski. Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements. IEEE Transactions on Signal Processing, 65(8):1988-2003, 2017.

V. Lyzinski, K. Levin, D. E. Fishkind and C. E. Priebe. 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(179):1-34, 2016.

Conference Publications

H. Yan and K. Levin. Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models. Proc. NeurIPS, 2024 (accepted; to appear)

K. Levin, F. Roosta-Khorasani, M. W. Mahoney and C. E. Priebe. Out-of-sample extension of graph adjacency spectral embedding. Proc. ICML, 2018.

S. Settle, K. Levin, H. Kamper and K. Livescu. Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings. Proc. INTERSPEECH, 2017.

V. Braverman, H. Lang, K. Levin and M. Monemizadeh. Clustering problems on sliding windows. Proc. 27th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2016.

K. Levin, A. Jansen and B. Van Durme. Segmental acoustic indexing for zero resource keyword search. Proc. 40th International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.

V. Braverman, H. Lang, K. Levin and M. Monemizadeh. Clustering on Sliding Windows in Polylogarithmic Space. Proc. 35th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS), 2015.

K. Levin, K. Henry, A. Jansen and K. Livescu. Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings. Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2013.