Daniel Lewis Sussman
Daniel Lewis Sussman
Verified email at bu.edu - Homepage
Title
Cited by
Cited by
Year
Saturated reconstruction of a volume of neocortex
N Kasthuri, KJ Hayworth, DR Berger, RL Schalek, JA Conchello, ...
Cell 162 (3), 648-661, 2015
6942015
A consistent adjacency spectral embedding for stochastic blockmodel graphs
DL Sussman, M Tang, DE Fishkind, CE Priebe
Journal of the American Statistical Association 107 (499), 1119-1128, 2012
2302012
Statistical inference on random dot product graphs: a survey
A Athreya, DE Fishkind, M Tang, CE Priebe, Y Park, JT Vogelstein, ...
The Journal of Machine Learning Research 18 (1), 8393-8484, 2017
1192017
Universally consistent vertex classification for latent positions graphs
M Tang, DL Sussman, CE Priebe
The Annals of Statistics 41 (3), 1406-1430, 2013
1102013
Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
DE Fishkind, DL Sussman, M Tang, JT Vogelstein, CE Priebe
Arxiv preprint arXiv:1205.0309, 2012
992012
Consistent latent position estimation and vertex classification for random dot product graphs
DL Sussman, M Tang, CE Priebe
IEEE transactions on pattern analysis and machine intelligence 36 (1), 48-57, 2013
96*2013
Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
V Lyzinski, DL Sussman, M Tang, A Athreya, CE Priebe
Electronic journal of statistics 8 (2), 2905-2922, 2014
952014
A limit theorem for scaled eigenvectors of random dot product graphs
A Athreya, CE Priebe, M Tang, V Lyzinski, DJ Marchette, DL Sussman
Sankhya A 78 (1), 1-18, 2016
91*2016
A semiparametric two-sample hypothesis testing problem for random graphs
M Tang, A Athreya, DL Sussman, V Lyzinski, Y Park, CE Priebe
Journal of Computational and Graphical Statistics 26 (2), 344-354, 2017
90*2017
A nonparametric two-sample hypothesis testing problem for random graphs
M Tang, A Athreya, DL Sussman, V Lyzinski, CE Priebe
Bernoulli 23 (3), 1599-1630, 2017
74*2017
Spectral clustering for divide-and-conquer graph matching
V Lyzinski, DL Sussman, DE Fishkind, H Pao, L Chen, JT Vogelstein, ...
Parallel Computing 47, 70-87, 2015
462015
Statistical inference on errorfully observed graphs
CE Priebe, DL Sussman, M Tang, JT Vogelstein
Journal of Computational and Graphical Statistics 24 (4), 930-953, 2015
402015
Elements of estimation theory for causal effects in the presence of network interference
DL Sussman, EM Airoldi
arXiv preprint arXiv:1702.03578, 2017
332017
Association between visceral adiposity and colorectal polyps on CT colonography
RM Summers, J Liu, DL Sussman, AJ Dwyer, B Rehani, PJ Pickhardt, ...
American Journal of Roentgenology 199 (1), 48-57, 2012
272012
Computing scalable multivariate glocal invariants of large (brain-) graphs
D Mhembere, WG Roncal, D Sussman, CE Priebe, R Jung, S Ryman, ...
2013 IEEE Global Conference on Signal and Information Processing, 297-300, 2013
242013
Empirical Bayes estimation for the stochastic blockmodel
S Suwan, DS Lee, R Tang, DL Sussman, M Tang, CE Priebe
Electronic Journal of Statistics 10 (1), 761-782, 2016
222016
Fully automated adipose tissue measurement on abdominal CT
J Yao, DL Sussman, RM Summers
Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and …, 2011
222011
Connectome Smoothing via Low-rank Approximations
R Tang, M Ketcha, A Badea, ED Calabrese, DS Margulies, JT Vogelstein, ...
IEEE transactions on medical imaging, 2018
182018
Matched filters for noisy induced subgraph detection
DL Sussman, Y Park, CE Priebe, V Lyzinski
IEEE transactions on pattern analysis and machine intelligence 42 (11), 2887 …, 2019
142019
Matchability of heterogeneous networks pairs
V Lyzinski, DL Sussman
Information and Inference, 0
13*
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Articles 1–20