1. E. W. Bridgeford et al. What is Connectome Coding? Current Opinion in Neurobiology, 2018.
  2. J. Browne et al. Forest Packing: Fast, Parallel Decision Forests. arXiv:1806.07300, 2018.
  3. G. Kiar et al. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. bioRxiv, 2018.
  4. C. E. Priebe et al. On a 'Two Truths' Phenomenon in Spectral Graph Clustering. arXiv:1808.07801, 2018.
  5. C. Shen and J. T. Vogelstein. The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. arXiv:1806.05514, 2018.
  6. T. M. Tomita et al. Random Projection Forests. arXiv:1506.03410, 2018.
  7. S. Wang et al. Signal Subgraph Estimation Via Vertex Screening. arXiv:1801.07683, 2018.
  8. Y. Lee, C. Shen and J. T. Vogelstein. Network Dependence Testing via Diffusion Maps and Distance-Based Correlations. arXiv:1703.10136, 2017.
  9. R. Tang et al. Robust Estimation from Multiple Graphs under Gross Error Contamination. arXiv:1707.03487, 2017.
  10. J. T. Vogelstein et al. Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data. arXiv:1709.01233, 2017.
  11. S. Wang, J. T. Vogelstein and C. E. Priebe. Joint Embedding of Graphs. arXiv:1703.03862, 2017.
  12. C. Shen et al. Discovering and Deciphering Relationships Across Disparate Data Modalities. arXiv:1609.05148, 2016.
  13. R. Tang et al. Connectome Smoothing via Low-rank Approximations. arXiv:1609.01672, 2016.
  14. V. Lyzinski et al. Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability. arXiv:1401.3813, 2014.
Peer-Reviewed Journal Publications
  1. A. Athreya et al. Statistical Inference on Random Dot Product Graphs: a Survey. Journal of Machine Learning Research, (226)18:1-92, 2018.
  2. R. Burns et al. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data. arXiv:1804.02835, 2018.
  3. J. D. Cohen et al. Detection and localization of surgically resectable cancers with a multi- analyte blood test. Science, (January)3247:1-10, 2018.
  4. C. Shen, C. E. Priebe and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation. Journal of the American Statistical Association, 2018.
  5. N. Binkiewicz, J. T. Vogelstein and K. Rohe. Covariate-assisted spectral clustering. Biometrika, (2)104:361-377, 2017.
  6. S. Chen et al. An M-estimator for reduced-rank system identification. Pattern Recognition Letters, 2017.
  7. D. Durante, D. B. Dunson and J. T. Vogelstein. Rejoinder: Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association, (520)112:1547-1552, 2017.
  8. D. Durante, D. B. Dunson and J. T. Vogelstein. Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association, (520)112:1516-1530, 2017.
  9. D. G. C. Hildebrand et al. Whole-brain serial-section electron microscopy in larval zebrafish. Nature, (7654)545:345, 2017.
  10. G. Kiar et al. Science in the cloud (SIC): A use case in MRI connectomics. GigaScience, (5)6:1-10, 2017.
  11. C. Shen, J. T. Vogelstein and C. E. Priebe. Manifold matching using shortest-path distance and joint neighborhood selection. Pattern Recognition Letters, 2017.
  12. A. K. Simhal et al. Probabilistic fluorescence-based synapse detection. PLoS Computational Biology, (4)13:e1005493, 2017.
  13. D. Zheng et al. Semi-external memory sparse matrix multiplication for billion-node graphs. IEEE Transactions on Parallel and Distributed Systems, (5)28:1470-1483, 2017.
  14. R. D. Airan et al. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Human Brain Mapping, (5)37:1986-1997, 2016.
  15. L. Chen et al. Robust Vertex Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, (3)38:578-590, 2016.
  16. E. L. Dyer et al. Quantifying mesoscale neuroanatomy using X-ray microtomography. eNeuro, (5)4:ENEURO--0195, 2016.
  17. D. Koutra et al. DeltaCon: Principled Massive-Graph Similarity Function with Attribution. ACM Trans. Knowl. Discov. Data, (3)10:28:1-28:43, 2016.
  18. V. Lyzinski et al. Graph Matching: Relax at Your Own Risk. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1)38:60-73, 2016.
  19. L. Chen et al. A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes. Worm, (2)5:e1142041, 2015.
  20. K. M. Harris et al. A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development. Scientific Data, 2015.
  21. N. Kasthuri et al. Saturated Reconstruction of a Volume of Neocortex. Cell, (3)162:648-661, 2015.
  22. V. Lyzinski et al. Spectral clustering for divide-and-conquer graph matching. Parallel Computing, 2015.
  23. C. E. Priebe et al. Statistical Inference on Errorfully Observed Graphs. Journal of Computational and Graphical Statistics, (4)24:930-953, 2015.
  24. J. T. Vogelstein et al. Fast Approximate Quadratic programming for graph matching. PLoS ONE, (4)10:e0121002, 2015.
  25. J. T. Vogelstein and C. E. Priebe. Shuffled Graph Classification: Theory and Connectome Applications. Journal of Classification, (1)32:3-20, 2015.
  26. W. R. Gray Roncal et al. An automated images-to-graphs framework for high resolution connectomics. Frontiers in Neuroinformatics, 2015.
  27. D. E. Carlson et al. Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Transactions on Biomedical Engineering, (1)61:41-54, 2014.
  28. E. M. Sweeney et al. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. PLoS ONE, (4)9:e95753, 2014.
  29. J. T. Vogelstein et al. Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning. Science, (6182)344:1-9, 2014.
  30. N. C. Weiler et al. Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography. Scientific Data, 2014.
  31. R. C. Craddock et al. Imaging human connectomes at the macroscale. Nature Methods, (6)10:524-539, 2013.
  32. D. Dai et al. Accurate prediction of AD patients using cortical thickness networks. Machine Vision and Applications, (7)24:1445-1457, 2013.
  33. C. E. Priebe, J. Vogelstein and D. Bock. Optimizing the quantity/quality trade-off in connectome inference. Communications in Statistics - Theory and Methods, (19)42:3455-3462, 2013.
  34. J. T. Vogelstein et al. Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, (7)35:1539-1551, 2013.
  35. D. E. Fishkind et al. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM Journal on Matrix Analysis and Applications, (1)34:23-39, 2012.
  36. W. R. Gray et al. Magnetic Resonance Connectome Automated Pipeline: An Overview. IEEE Pulse, (2)3:42-48, 2012.
  37. N. J. Roberts et al. The predictive capacity of personal genome sequencing. Science Translational Medicine, (133)4:133ra58, 2012.
  38. S. B. Hofer et al. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nature Neuroscience, (8)14:1045-1052, 2011.
  39. Y. Mishchencko, J. T. Vogelstein and L. Paninski. A Bayesian approach for inferring neuronal conectivity from calcium fluorescent imaging data. The annals of applied statistics, (2)5:1229-1261, 2011.
  40. J. T. Vogelstein, R. J. Vogelstein and C. E. Priebe. Are mental properties supervenient on brain properties? Scientific Reports, 2011.
  41. L. Paninski et al. A new look at state-space models for neural data. Journal of Computational Neuroscience, (1-2)29:107-126, 2010.
  42. J. T. Vogelstein et al. Fast non-negative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, (6)104:3691-3704, 2009.
  43. J. T. Vogelstein et al. Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal, (2)97:636-655, 2009.
  44. R. J. Vogelstein et al. Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses. IEEE Transactions on Neural Networks, (1)18:253-265, 2007.
  45. J. T. Vogelstein, L. H. Snyder and D. E. Angelaki. Accuracy of saccades to remembered targets as a function of body orientation in space. Journal of neurophysiology, (1)90:521-524, 2003.
  46. D. L. Greenspan et al. Loss of FHIT expression in cervical carcinoma cell lines and primary tumors. Cancer Research, (21)57:4692-4698, 1997.
Peer-Reviewed Conference Proceedings
  1. K. Lillaney et al. Building NDStore through Hierarchical Storage Management and Microservice Processing. IEEE International Conference on eScience, 2018.
  2. A. Nikolaidis et al. Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC. CCN 2018, 2018.
  3. D. Zheng et al. FlashR: Parallelize and scale R for machine learning using SSDs. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, (1)Part F1344:183-194, 2018.
  4. D. Mhembere et al. knor : A NUMA-Optimized In-Memory , Distributed and Semi-External-Memory k-means Library. Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, 2017.
  5. T. Tomita, M. Maggioni and J. Vogelstein. ROFLMAO: Robust oblique forests with linear MAtrix operations. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017, 2017.
  6. K. S. Kutten et al. A Diffeomorphic Approach to Multimodal Registration with Mutual Information: Applications to CLARITY Mouse Brain Images. MICCAI, 2016.
  7. K. S. Kutten et al. Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM. SPIE Europe, 2016.
  8. W. Gray Roncal et al. VESICLE : Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale. British Machine Vision Conference, 2015.
  9. D. Zheng et al. FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. USENIX Conference on File and Storage Technologies, 2014.
  10. R. Burns et al. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. Proceedings of the 25th International Conference on Scientific and Statistical Database Management, 2013.
  11. D. E. Carlson et al. Real-Time Inference for a Gamma Process Model of Neural Spiking. Advances in Neural Information Processing Systems 26, 2013.
  12. B. Cornelis et al. Bayesian crack detection in ultra high resolution multimodal images of paintings. 2013 18th International Conference on Digital Signal Processing, DSP 2013, 2013.
  13. M. Fiori et al. Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching. Advances in Neural Information Processing Systems, 2013.
  14. D. Koutra, J. T. Vogelstein and C. Faloutsos. DELTACON: A Principled Massive-Graph Similarity Function. Proceedings of the 2013 SIAM International Conference on Data Mining, 2013.
  15. V. Kulkarni et al. Sex differences in the Human Connectome. International Conference on Brain and Health Informatics, 2013.
  16. D. Mhembere et al. Computing scalable multivariate glocal invariants of large (brain-) graphs. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013.
  17. F. Petralia, J. Vogelstein and D. B. Dunson. Multiscale Dictionary Learning for Estimating Conditional Distributions. Nips, 2013.
  18. W. G. Roncal et al. MIGRAINE: MRI graph reliability analysis and inference for connectomics. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013.
  19. Q. J. Huys, J. Vogelstein and P. Dayan. Psychiatry: Insights into depression through normative decision-making models. Advances in Neural Information Processing Systems (NIPS), 2008.
Technical Reports
  1. C. E. Priebe et al. Semiparametric spectral modeling of the Drosophila connectome. arXiv:1705.03297, 2017.
  2. D. Zheng et al. An SSD-based eigensolver for spectral analysis on billion-node graphs. arXiv:1602.01421, 2016.
  3. D. Zheng et al. FlashMatrix: Parallel, Scalable Data Analysis with Generalized Matrix Operations using Commodity SSDs. arXiv:1604.06414, 2016.
  4. A. Banerjee, J. Vogelstein and D. Dunson. Parallel inversion of huge covariance matrices. arXiv:1312.1869, 2013.
  5. M. Kazhdan et al. Gradient-Domain Processing for Large EM Image Stacks. arXiv:1310.0041, 2013.
Other Publications
  1. G. Kiar et al. NeuroStorm: Accelerating Brain Science Discovery in the Cloud. arXiv:1803.03367, 2018.
  2. N. C. Consortium. To the cloud! A grassroots proposal to accelerate brain science discovery. Neuron, (3)92:622-627, 2016.
  3. Global Brain Workshop. Grand challenges for global brain sciences. F1000Research, 2016.
  4. R. Burns, J. T. Vogelstein and A. S. Szalay. From cosmos to connectomes: The evolution of data-intensive science. Neuron, (6)83:1249-1252, 2014.
  5. P. Golland et al. A New Age of Computing and the Brain: Report of the CCC Brain Workshop. CCC Brain Workshop, 2014.
  6. J. T. Vogelstein. Q&A: What is the Open Connectome Project? Neural Systems & Circuits, (1)1:16, 2011.
  7. R. Yuste et al. Imaging action potentials with calcium indicators. Cold Spring Harbor Protocols, (8)6:985-989, 2011.
  8. J. T. Vogelstein, J. V. Vogelstein and B. Vogelstein. NIH Grant Application Testing the effects of genetic variations using MINIME technology. Science, (5448)286:2300-2301, 1999.