Technical Reports
  1. T. L. Athey, M. A. Wright, M. Pavlovic, V. Chandrashekhar, K. Deisseroth, M. I. Miller, and J. T. Vogelstein. BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes. Neuroinformatics, 2023.
  2. V. Chandrashekhar, D. J. Tward, D. Crowley, A. K. Crow, M. A. Wright, B. Y. Hsueh, F. Gore, T. A. Machado, A. Branch, J. S. Rosenblum, K. Deisseroth, and J. T. Vogelstein. CloudReg: automatic terabyte-scale cross-modal brain volume registration. Nature Methods, 2021.
  3. H. S. Helm, R. D. Mehta, B. Duderstadt, W. Yang, C. M. White, A. Geisa, J. T. Vogelstein, and C. E. Priebe. A partition-based similarity for classification distributions. arXiv, 2020.
  4. Jaewon Chung, Eric Bridgeford, Jesus Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua Vogelstein. Statistical Connectomics. arXiv, 2020.
  5. J. T. Vogelstein. P-Values in a Post-Truth World. arXiv, 2020.
  6. H. S. Helm, A. Basu, A. Athreya, Y. Park, J. T. Vogelstein, M. Winding, M. Zlatic, A. Cardona, P. Bourke, J. Larson, C. White, and C. E. Priebe. Learning to rank via combining representations. arXiv, 2020.
  7. C. E. Priebe, J. T. Vogelstein, F. Engert, and C. M. White. Modern Machine Learning: Partition Vote. bioRxiv, 2020.
  8. Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, and Joshua T. Vogelstein. A New Age of Computing and the Brain. arXiv, 2020.
  9. Tyler M. Tomita and Joshua T. Vogelstein. Robust Similarity and Distance Learning via Decision Forests. arXiv, 2020.
  10. Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. Statistical Analysis of Data Repeatability Measures. arXiv, 2020.
  11. D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. Graphyti: A Semi-External Memory Graph Library for FlashGraph. arXiv, 2019.
  12. H. Helm, J. V. Vogelstein, and C. E. Priebe. Vertex Classification on Weighted Networks. arXiv, 2019.
  13. J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing. arXiv, 2019.
  14. D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. clusterNOR: A NUMA-Optimized Clustering Framework. arxiv, 2019.
  15. 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.
  16. 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.
  17. 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.
  18. 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. Vald├ęs-Sosa, Q. Wang, M. I. Miller, R. Burns, and J. T. Vogelstein. NeuroStorm: Accelerating Brain Science Discovery in the Cloud. arXiv, 2018.
  19. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.
  20. 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.
  21. R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. Robust Estimation from Multiple Graphs under Gross Error Contamination. arXiv, 2017.
  22. 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.
  23. 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, 2017.
  24. 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.
  25. D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. Flashmatrix: parallel, scalable data analysis with generalized matrix operations using commodity ssds. arXiv, 2016.
  26. A. Sinha, W. Roncal, and N. Kasthuri. Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes. arXiv, 2014.
  27. M. Kazhdan, R. Burns, B. Kasthuri, J. Lichtman, J. Vogelstein, and J. Vogelstein. Gradient-Domain Processing for Large EM Image Stacks. arXiv, 2013.
  28. A. Banerjee, J. Vogelstein, and D. Dunson. Parallel inversion of huge covariance matrices. arXiv, 2013.