Publications

Pre-prints
  1. E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, T. Xu, C. Craddock, G. Kiar, W. Gray-Roncal, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. Optimal Experimental Design for Big Data: Applications in Brain Imaging. bioRxiv, 2019.
  2. R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. A Consistent Independence Test for Multivariate Time-Series. arxiv, 2019.
  3. R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. Manifold Forests: Closing the Gap on Neural Networks. arXiv, 2019.
  4. M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets. bioRxiv, 2019.
  5. T. M. Tomita, J. Browne, C. Shen, J. Chung, J. L. Patsolic, B. Falk, J. Yim, C. E. Priebe, R. Burns, M. Maggioni, and J. T. Vogelstein. Sparse Projection Oblique Randomer Forests. arXiv, 2019.
  6. R. Guo, C. Shen, and J. T. Vogelstein. Estimating Information-Theoretic Quantities with Random Forests. arXiv, 2019.
  7. S. Hong, J. T. Vogelstein, G. Gozzi, B. C. Bernhardt, T. B. Yeo, M. P. Milham, and A. Di Martino. Towards Neurosubtypes in Autism. bioRxiv, 2019.
  8. M. Madhyastha, P. Li, J. Browne, V. Strnadova-Neely, C. E. Priebe, R. Burns, and J. T. Vogelstein. Geodesic Learning via Unsupervised Decision Forests. arXiv, 2019.
  9. D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. Graphyti: A Semi-External Memory Graph Library for FlashGraph. arXiv, 2019.
  10. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. T. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. bioRxiv, 2019.
  11. S. Panda, S. Palaniappan, J. Xiong, A. Swaminathan, S. Ramachandran, E. W. Bridgeford, C. Shen, and J. T. Vogelstein. mgcpy: A Comprehensive High Dimensional Independence Testing Python Package. arXiv, 2019.
  12. N. Wang, R. J. Anderson, D. G. Ashbrook, V. Gopalakrishnan, Y. Park, C. E. Priebe, Y. Qi, J. T. Vogelstein, R. W. Williams, and A. G. Johnson. Node-Specific Heritability in the Mouse Connectome. bioRxiv, 2019.
  13. T. Xu, K. Nenning, E. Schwartz, S. Hong, J. T. Vogelstein, D. A. Fair, C. E. Schroeder, D. S. Margulies, J. Smallwood, M. P. Milham, and G. Langs. Cross-species Functional Alignment Reveals Evolutionary Hierarchy Within the Connectome. bioRxiv, 2019.
  14. J. Arroyo, A. Athreya, J. Cape, G. Chen, C. E. Priebe, and J. T. Vogelstein. Inference for multiple heterogenous networks with a common invariant subspace. arXiv, 2019.
  15. H. Helm, J. V. Vogelstein, and C. E. Priebe. Vertex Classification on Weighted Networks. arXiv, 2019.
  16. J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing. arXiv, 2019.
  17. D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. clusterNOR: A NUMA-Optimized Clustering Framework. arxiv, 2019.
  18. A. Branch, D. Tward, J. T. Vogelstein, Z. Wu, and M. Gallagher. An optimized protocol for iDISCO+ rat brain clearing, imaging, and analysis. bioRxiv, 2019.
  19. C. Shen and J. T. Vogelstein. Decision Forests Induce Characteristic Kernels. arXiv, 2018.
  20. D. S. Greenberg, D. J. Wallace, K. Voit, S. Wuertenberger, U. Czubayko, A. Monsees, T. Handa, J. T. Vogelstein, R. Seifert, Y. Groemping, and J. N. Kerr. Accurate action potential inference from a calcium sensor protein through biophysical modeling. bioRxiv, 2018.
  21. J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. Geometric Dimensionality Reduction for Subsequent Classification. arXiv, 2018.
  22. Z. Wang, H. Sair, C. Crainiceanu, M. Lindquist, B. A. Landman, S. Resnick, J. T. Vogelstein, and B. S. Caffo. On statistical tests of functional connectome fingerprinting. bioRxiv, 2018.
  23. C. Shen and J. T. Vogelstein. The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. arXiv, 2018.
  24. G. Kiar, E. Bridgeford, W. G. Roncal, (CoRR), V. Chandrashekhar, D. Mhembere, S. Ryman, X. Zuo, D. S. Marguiles, R. C. Craddock, C. E. Priebe, R. Jung, V. Calhoun, B. Caffo, R. Burns, M. P. Milham, and J. Vogelstein. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. bioRxiv, 2018.
  25. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.
  26. G. Kiar, E. Bridgeford, V. Chandrashekhar, D. Mhembere, R. Burns, W. R. G. Roncal, and J. T. Vogelstein. A comprehensive cloud framework for accurate and reliable human connectome estimation and meganalysis. bioRxiv, 2017.
  27. G. Franca, M. L. Rizzo, and J. T. Vogelstein. Kernel k-Groups via Hartigan's Method. arXiv, 2017.
  28. R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. Robust Estimation from Multiple Graphs under Gross Error Contamination. arXiv, 2017.
  29. H. Patsolic, S. Adali, J. T. Vogelstein, Y. Park, C. E. Priebe, G. Li, and V. Lyzinski. Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability. arXiv, 2014.
Peer-Reviewed Journal Publications
  1. S. Wang, J. Arroyo, J. T. Vogelstein, and C. E. Priebe. Joint Embedding of Graphs. Transactions on Pattern Analysis and Machine Intelligence, 2019.
  2. Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein. Network dependence testing via diffusion maps and distance-based correlations. Biometrika, 2019.
  3. J. Chung, B. D. Pedigo, E. W. Bridgeford, B. K. Varjavand, and J. T. Vogelstein. GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, (158)20:1-7, 2019.
  4. 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, 2019.
  5. C. E. Priebe, Y. Park, J. T. Vogelstein, J. M. Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, and E. Bridgeford. On a two-truths phenomenon in spectral graph clustering. Proceedings of the National Academy of Sciences of the United States of America, (13)116:5995-6000, 2019.
  6. J. J. Son, J. C. Clucas, C. White, A. Krishnakumar, J. T. Vogelstein, M. P. Milham, and A. Klein. Thermal sensors improve wrist-worn position tracking. npj digital medicine, 2019.
  7. J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, and C. Shen. Discovering and deciphering relationships across disparate data modalities. eLife, 2019.
  8. R. Tang, M. Ketcha, A. Badea, E. D. Calabrese, D. S. Margulies, J. T. Vogelstein, C. E. Priebe, and D. L. Sussman. Connectome Smoothing via Low-rank Approximations. Transactions in Medical Imaging, 2018.
  9. C. Shen, C. E. Priebe, and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation. Journal of the American Statistical Association, 2018.
  10. J. T. Vogelstein, E. Perlman, B. Falk, A. Baden, W. Gray Roncal, V. Chandrashekhar, F. Collman, S. Seshamani, J. L. Patsolic, K. Lillaney, M. Kazhdan, R. Hider, D. Pryor, J. Matelsky, T. Gion, P. Manavalan, B. Wester, M. Chevillet, E. T. Trautman, K. Khairy, E. Bridgeford, D. M. Kleissas, D. J. Tward, A. K. Crow, B. Hsueh, M. A. Wright, M. I. Miller, S. J. Smith, R. J. Vogelstein, K. Deisseroth, and R. Burns. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data. Nature Methods, (11)15:846-847, 2018.
  11. A. Athreya, D. E. Fishkind, M. Tang, C. E. Priebe, Y. Park, J. T. Vogelstein, K. Levin, V. Lyzinski, Y. Qin, and D. L. Sussman. Statistical Inference on Random Dot Product Graphs: a Survey. Journal of Machine Learning Research, 2018.
  12. J. D. Cohen, L. Li, Y. Wang, C. Thoburn, B. Afsari, L. Danilova, C. Douville, A. A. Javed, F. Wong, A. Mattox, R. H. Hruban, C. L. Wolfgang, M. G. Goggins, M. D. Molin, T. L. Wang, R. Roden, A. P. Klein, J. Ptak, L. Dobbyn, J. Schaefer, N. Silliman, M. Popoli, J. T. Vogelstein, J. D. Browne, R. E. Schoen, R. E. Brand, J. Tie, P. Gibbs, H. L. Wong, A. S. Mansfield, J. Jen, S. M. Hanash, M. Falconi, P. J. Allen, S. Zhou, C. Bettegowda, L. A. Diaz, C. Tomasetti, K. W. Kinzler, B. Vogelstein, A. M. Lennon, and N. Papadopoulos. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science, (6378)359:926-930, 2018.
  13. D. Durante, D. B. Dunson, and J. T. Vogelstein. Rejoinder: Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association, 2017.
  14. G. Kiar, K. J. Gorgolewski, D. Kleissas, W. G. Roncal, B. Litt, B. Wandell, R. A. Poldrack, M. Wiener, R. J. Vogelstein, R. Burns, and J. T. Vogelstein. Science in the cloud (SIC): A use case in MRI connectomics. GigaScience, (5)6:1-10, 2017.
  15. S. Chen, K. Liu, Y. Yang, Y. Xu, S. Lee, M. Lindquist, B. S. Caffo, and J. T. Vogelstein. An M-estimator for reduced-rank system identification. Pattern Recognition Letters, 2017.
  16. N. Binkiewicz, J. T. Vogelstein, and K. Rohe. Covariate-assisted spectral clustering. Biometrika, (2)104:361-377, 2017.
  17. 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.
  18. D. G. C. Hildebrand, M. Cicconet, R. M. Torres, W. Choi, T. M. Quan, J. Moon, A. W. Wetzel, A. Scott Champion, B. J. Graham, O. Randlett, G. S. Plummer, R. Portugues, I. H. Bianco, S. Saalfeld, A. D. Baden, K. Lillaney, R. Burns, J. T. Vogelstein, A. F. Schier, W. C. A. Lee, W. K. Jeong, J. W. Lichtman, and F. Engert. Whole-brain serial-section electron microscopy in larval zebrafish. Nature, (7654)545:345-349, 2017.
  19. C. Shen, J. T. Vogelstein, and C. E. Priebe. Manifold matching using shortest-path distance and joint neighborhood selection. Pattern Recognition Letters, 2017.
  20. A. K. Simhal, C. Aguerrebere, F. Collman, J. T. Vogelstein, K. D. Micheva, R. J. Weinberg, S. J. Smith, and G. Sapiro. Probabilistic fluorescence-based synapse detection. PLoS Computational Biology, 2017.
  21. Q. Wang, M. Zhang, T. Tomita, J. T. Vogelstein, S. Zhou, N. Papadopoulos, K. W. Kinzler, and B. Vogelstein. Selected reaction monitoring approach for validating peptide biomarkers. Proceedings of the National Academy of Sciences of the United States of America, (51)114:13519-13524, 2017.
  22. D. Zheng, D. Mhembere, V. Lyzinski, J. T. Vogelstein, C. E. Priebe, and R. Burns. Semi-external memory sparse matrix multiplication for billion-node graphs. IEEE Transactions on Parallel and Distributed Systems, (5)28:1470-1483, 2017.
  23. D. Koutra, N. Shah, J. T. Vogelstein, B. Gallagher, and C. Faloutsos. DeltaCon: Principled Massive-Graph Similarity Function with Attribution. ACM Transactions on Knowledge Discovery from Data, 2016.
  24. V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe, and G. Sapiro. Graph Matching: Relax at Your Own Risk. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1)38:60-73, 2016.
  25. R. D. Airan, J. T. Vogelstein, J. J. Pillai, B. Caffo, J. J. Pekar, and H. I. Sair. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Human Brain Mapping, (5)37:1986-1997, 2016.
  26. L. Chen, C. Shen, J. T. Vogelstein, and C. E. Priebe. Robust Vertex Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, (3)38:578-590, 2016.
  27. E. L. Dyer, W. G. Roncal, H. L. Fernandes, D. Gürsoy, V. De Andrade, R. Vescovi, K. Fezzaa, X. Xiao, J. T. Vogelstein, C. Jacobsen, K. P. Körding, and N. Kasthuri. Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography. eNeuro, 2016.
  28. C. E. Priebe, D. L. Sussman, M. Tang, and J. T. Vogelstein. Statistical Inference on Errorfully Observed Graphs. Journal of Computational and Graphical Statistics, (4)24:930-953, 2015.
  29. L. Chen, J. T. Vogelstein, V. Lyzinski, and C. E. Priebe. A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes. Worm, 2015.
  30. K. M. Harris, J. Spacek, M. E. Bell, P. H. Parker, L. F. Lindsey, A. D. Baden, J. T. Vogelstein, and R. Burns. A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development. Scientific Data, 2015.
  31. N. Kasthuri, K. J. Hayworth, D. R. Berger, R. L. Schalek, J. A. Conchello, S. Knowles-Barley, D. Lee, A. Vázquez-Reina, V. Kaynig, T. R. Jones, M. Roberts, J. L. Morgan, J. C. Tapia, H. S. Seung, W. G. Roncal, J. T. Vogelstein, R. Burns, D. L. Sussman, C. E. Priebe, H. Pfister, and J. W. Lichtman. Saturated Reconstruction of a Volume of Neocortex. Cell, (3)162:648-661, 2015.
  32. V. Lyzinski, D. L. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. T. Vogelstein, Y. Park, and C. E. Priebe. Spectral clustering for divide-and-conquer graph matching. Parallel Computing, 2015.
  33. J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, and C. E. Priebe. Fast Approximate Quadratic programming for graph matching. PLoS ONE, 2015.
  34. J. T. Vogelstein and C. E. Priebe. Shuffled Graph Classification: Theory and Connectome Applications. Journal of Classification, (1)32:3-20, 2015.
  35. W. R. Gray Roncal, D. M. Kleissas, J. T. Vogelstein, P. Manavalan, K. Lillaney, M. Pekala, R. Burns, R. J. Vogelstein, C. E. Priebe, M. A. Chevillet, and G. D. Hager. An automated images-to-graphs framework for high resolution connectomics. Frontiers in Neuroinformatics, 2015.
  36. D. E. Carlson, J. T. Vogelstein, Q. Wu, W. Lian, M. Zhou, C. R. Stoetzner, D. Kipke, D. Weber, D. B. Dunson, and L. Carin. Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Transactions on Biomedical Engineering, (1)61:41-54, 2014.
  37. E. M. Sweeney, J. T. Vogelstein, J. L. Cuzzocreo, P. A. Calabresi, D. S. Reich, C. M. Crainiceanu, and R. T. Shinohara. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. PLoS ONE, 2014.
  38. J. T. Vogelstein, Y. Park, T. Ohyama, R. A. Kerr, J. W. Truman, C. E. Priebe, and M. Zlatic. Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning. Science, (6182)344:386-392, 2014.
  39. N. C. Weiler, F. Collman, J. T. Vogelstein, R. Burns, and S. J. Smith. Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography. Scientific Data, 2014.
  40. R. C. Craddock, S. Jbabdi, C. G. Yan, J. T. Vogelstein, F. X. Castellanos, A. Di Martino, C. Kelly, K. Heberlein, S. Colcombe, and M. P. Milham. Imaging human connectomes at the macroscale. Nature Methods, (6)10:524-539, 2013.
  41. D. Dai, H. He, J. T. Vogelstein, and Z. Hou. Accurate prediction of AD patients using cortical thickness networks. Machine Vision and Applications, (7)24:1445-1457, 2013.
  42. 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.
  43. J. T. Vogelstein, W. G. Roncal, R. J. Vogelstein, and C. E. Priebe. Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, (7)35:1539-1551, 2013.
  44. W. R. Gray, J. A. Bogovic, J. T. Vogelstein, B. A. Landman, J. L. Prince, and R. J. Vogelstein. Magnetic Resonance Connectome Automated Pipeline: An Overview. IEEE Pulse, (2)3:42-48, 2012.
  45. D. E. Fishkind, D. L. Sussman, M. Tang, J. T. Vogelstein, and C. E. Priebe. 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.
  46. N. J. Roberts, J. T. Vogelstein, G. Parmigiani, K. W. Kinzler, B. Vogelstein, and V. E. Velculescu. The predictive capacity of personal genome sequencing. Science Translational Medicine, 2012.
  47. S. B. Hofer, H. Ko, B. Pichler, J. Vogelstein, H. Ros, H. Zeng, E. Lein, N. A. Lesica, and T. D. Mrsic-Flogel. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nature Neuroscience, (8)14:1045-1052, 2011.
  48. 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, 2011.
  49. J. T. Vogelstein, R. J. Vogelstein, and C. E. Priebe. Are mental properties supervenient on brain properties? Scientific Reports, 2011.
  50. L. Paninski, Y. Ahmadian, D. G. Ferreira, S. Koyama, K. Rahnama Rad, M. Vidne, J. Vogelstein, and W. Wu. A new look at state-space models for neural data. Journal of Computational Neuroscience, (1-2)29:107-126, 2010.
  51. J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski. Fast non-negative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 2009.
  52. J. T. Vogelstein, B. O. Watson, A. M. Packer, R. Yuste, B. Jedynak, and L. Paninski. Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal, (2)97:636-655, 2009.
  53. R. J. Vogelstein, U. Mallik, J. T. Vogelstein, and G. Cauwenberghs. Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses. IEEE Transactions on Neural Networks, (1)18:253-265, 2007.
  54. 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.
  55. D. L. Greenspan, D. C. Connolly, R. Wu, R. Y. Lei, J. T. Vogelstein, Y. T. Kim, J. E. Mok, N. Muñoz, F. X. Bosch, K. Shah, and K. R. Cho. Loss of FHIT expression in cervical carcinoma cell lines and primary tumors. Cancer Research, 1997.
Peer-Reviewed Conference Proceedings
  1. J. Browne, D. Mhembere, T. M. Tomita, J. T. Vogelstein, and R. Burns. Forest packing: Fast Parallel, Decision Forests. SIAM International Conference on Data Mining, SDM 2019, 2019.
  2. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. bioRxiv, 2019.
  3. K. Lillaney, D. Kleissas, A. Eusman, E. Perlman, W. Gray Roncal, J. T. Vogelstein, and R. Burns. Building NDStore through hierarchical storage management and microservice processing. Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, 2018.
  4. K. S. Kutten, N. Charon, M. I. Miller, J. T. Ratnanather, J. Matelsky, A. D. Baden, K. Lillaney, K. Deisseroth, L. Ye, and J. T. Vogelstein. A large deformation diffeomorphic approach to registration of CLARITY images via mutual information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017.
  5. D. Mhembere, C. E. Priebe, J. T. Vogelstein, and R. Burns. 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.
  6. T. M. Tomita, M. Maggioni, and J. T. Vogelstein. ROFLMAO: Robust oblique forests with linear MAtrix operations. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017, 2017.
  7. D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs. PPoPP, 2016.
  8. K. S. Kutten, J. T. Vogelstein, N. Charon, L. Ye, K. Deisseroth, and M. I. Miller. Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM. Optics, Photonics and Digital Technologies for Imaging Applications IV, 2016.
  9. W. G. Roncal, M. Pekala, V. Kaynig-Fittkau, D. M. Kleissas, J. T. Vogelstein, H. Pfister, R. Burns, R. J. Vogelstein, M. A. Chevillet, and G. D. Hager. VESICLE: Volumetric Evaluation of Synaptic Inferfaces using Computer Vision at Large Scale. British Machine Vision Conference, 2015.
  10. D. Mhembere, W. Gray Roncal, D. Sussman, C. E. Priebe, R. Jung, S. Ryman, R. J. Vogelstein, J. T. Vogelstein, and R. Burns. Computing scalable multivariate glocal invariants of large (brain-) graphs. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013.
  11. W. G. Roncal, Z. H. Koterba, D. Mhembere, D. M. Kleissas, J. T. Vogelstein, R. Burns, A. R. Bowles, D. K. Donavos, S. Ryman, R. E. Jung, L. Wu, V. Calhoun, and R. J. Vogelstein. MIGRAINE: MRI graph reliability analysis and inference for connectomics. 2013 IEEE Global Conference on Signal and Information Processing, 2013.
  12. R. Burns, W. G. Roncal, D. Kleissas, K. Lillaney, P. Manavalan, E. Perlman, D. R. Berger, D. D. Bock, K. Chung, L. Grosenick, N. Kasthuri, N. C. Weiler, K. Deisseroth, M. Kazhdan, J. Lichtman, R. C. Reid, S. J. Smith, A. S. Szalay, J. T. Vogelstein, and R. J. Vogelstein. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. ACM International Conference Proceeding Series, 2013.
  13. D. E. Carlson, V. Rao, J. T. Vogelstein, and L. Carin. Real-Time Inference for a Gamma Process Model of Neural Spiking. Advances in Neural Information Processing Systems 26, 2013.
  14. B. Cornelis, Y. Yang, J. T. Vogelstein, A. Dooms, I. Daubechies, and D. Dunson. Bayesian crack detection in ultra high resolution multimodal images of paintings. 18th International Conference on Digital Signal Processing, 2013.
  15. M. Fiori, P. Sprechmann, J. Vogelstein, P. Muse, and G. Sapiro. Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching. Advances in Neural Information Processing Systems, 2013.
  16. 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, SDM 2013, 2013.
  17. V. Kulkarni, J. S. Pudipeddi, L. Akoglu, J. T. Vogelstein, R. J. Vogelstein, S. Ryman, and R. E. Jung. Sex differences in the human connectome. Brain and Health Informatics, 2013.
  18. F. Petralia, J. Vogelstein, and D. B. Dunson. Multiscale Dictionary Learning for Estimating Conditional Distributions. Advances in Neural Information Processing Systems, 2013.
  19. D. Zheng, D. Mhembere, R. Burns, J. T. Vogelstein, C. E. Priebe, and A. S. Szalay. FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. USENIX Conference on File and Storage Technologies, 2012.
  20. Q. J. Huys, J. Vogelstein, and P. Dayan. Psychiatry: Insights into depression through normative decision-making models. Advances in Neural Information Processing Systems, 2008.
Technical Reports
  1. G. Kiar, R. J. Anderson, A. Baden, A. Badea, E. W. Bridgeford, A. Champion, V. Chandrashekhar, F. Collman, B. Duderstadt, A. C. Evans, F. Engert, B. Falk, T. Glatard, W. R. G. Roncal, D. N. Kennedy, J. Maitin-Shepard, R. A. Marren, O. Nnaemeka, E. Perlman, S. Seshamani, E. T. Trautman, D. J. Tward, P. A. Valdés-Sosa, Q. Wang, M. I. Miller, R. Burns, and J. T. Vogelstein. NeuroStorm: Accelerating Brain Science Discovery in the Cloud. arXiv, 2018.
  2. C. E. Priebe, Y. Park, M. Tang, A. Athreya, V. Lyzinski, J. T. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, and A. Cardona. Semiparametric spectral modeling of the Drosophila connectome. arXiv, 2017.
  3. D. Zheng, R. Burns, J. Vogelstein, C. E. Priebe, and A. S. Szalay. An SSD-based eigensolver for spectral analysis on billion-node graphs. arXiv, 2016.
  4. D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs. CoRR, abs/1604.06414, 2016.
  5. A. Sinha, W. Roncal, and N. Kasthuri. Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes. arXiv, 2014.
  6. M. Kazhdan, R. Burns, B. Kasthuri, J. Lichtman, J. Vogelstein, and J. Vogelstein. Gradient-Domain Processing for Large EM Image Stacks. arXiv, 2013.
  7. A. Banerjee, J. Vogelstein, and D. Dunson. Parallel inversion of huge covariance matrices. arXiv, 2013.
Other Publications
  1. E. W. Bridgeford, D. Sussman, V. Lyzinski, Y. Qin, Y. Park, B. Caffo, C. E. Priebe, and J. T. Vogelstein. What is Connectome Coding? SfN 2018 course book, 2018.
  2. J. T. Vogelstein, K. Amunts, A. Andreou, D. Angelaki, G. A. Ascoli, C. Bargmann, R. Burns, C. Cali, F. Chance, G. Church, H. Cline, T. Coleman, D. Stephanie de La Rochefoucauld, A. B. Elgoyhen, R. E. Cummings, A. Evans, K. Harris, M. Hausser, S. Hill, S. Inverso, C. Jackson, V. Jain, R. Kass, B. Kasthuri, A. Kepecs, G. Kiar, K. Kording, S. P. Koushika, J. Krakauer, S. Landis, J. Layton, Q. Luo, A. Marblestone, D. Markowitz, J. McArthur, B. Mensh, M. P. . Milham, P. Mitra, P. Neskovic, M. Nicolelis, R. O'Brien, A. Oliva, G. Orban, H. Peng, E. Perlman, M. Picciotto, M. Poo, J. Poline, A. Pouget, S. Raghavachari, J. Roskams, A. P. Schaffer, T. Sejnowski, F. T. Sommer, N. Spruston, L. Swanson, A. Toga, R. J. Vogelstein, A. Zador, R. Huganir, and M. I. Miller. Grand challenges for global brain sciences. F1000Research, 2016.
  3. J. T. Vogelstein, B. Mensh, M. Häusser, N. Spruston, A. C. Evans, K. Kording, K. Amunts, C. Ebell, J. Muller, M. Telefont, S. Hill, S. P. Koushika, C. Calì, P. A. Valdés-Sosa, P. B. Littlewood, C. Koch, S. Saalfeld, A. Kepecs, H. Peng, Y. O. Halchenko, G. Kiar, M. M. Poo, J. B. Poline, M. P. Milham, A. P. Schaffer, R. Gidron, H. Okano, V. D. Calhoun, M. Chun, D. M. Kleissas, R. J. Vogelstein, E. Perlman, R. Burns, R. Huganir, and M. I. Miller. To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery. Neuron, (3)92:622-627, 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, J. Gallant, G. Hager, H. Pfister, C. Papadimitriou, S. Schaal, and J. T. Vogelstein. A New Age of Computing and the Brain: Report of the CCC Brain Workshop. CCC Brain Workshop, 2014.
  6. J. T. Vogelstein, W. G. Roncal, R. J. Vogelstein, and C. E. Priebe. Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, (7)35:1539-1551, 2013.
  7. J. T. Vogelstein. Q and A: What is the Open Connectome Project? Neural Systems and Circuits, 2011.
  8. R. Yuste, J. MacLean, J. Vogelstein, and L. Paninski. Imaging action potentials with calcium indicators. Cold Spring Harbor Protocols, (8)6:985-989, 2011.
  9. J. T. Vogelstein. Oopsi: a family of optimal optical spike inference algorithms for inferring neural connectivity from population calcium imaging. Learning, 2009.
  10. 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.