Reports


Technical Reports
  1. Joshua T. Vogelstein. P-Values in a Post-Truth World. arXiv, 2020.
  2. 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. None, 2020.
  3. 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.
  4. Joshua T. Vogelstein, Hayden S. Helm, Ronak D. Mehta, Jayanta Dey, Will LeVine, Weiwei Yang, Bryan Tower, Jonathan Larson, Chris White, and Carey E. Priebe. A general approach to progressive learning. None, 2020.
  5. Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, and Joshua T. Vogelstein. A New Age of Computing and the Brain. None, 2020.
  6. Tyler M. Tomita and Joshua T. Vogelstein. Robust Similarity and Distance Learning via Decision Forests. None, 2020.
  7. Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. Statistical Analysis of Data Repeatability Measures. arXiv, 2020.
  8. D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. Graphyti: A Semi-External Memory Graph Library for FlashGraph. arXiv, 2019.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. A. Sinha, W. Roncal, and N. Kasthuri. Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes. arXiv, 2014.
  16. M. Kazhdan, R. Burns, B. Kasthuri, J. Lichtman, J. Vogelstein, and J. Vogelstein. Gradient-Domain Processing for Large EM Image Stacks. arXiv, 2013.
  17. A. Banerjee, J. Vogelstein, and D. Dunson. Parallel inversion of huge covariance matrices. arXiv, 2013.