Posters

  1. B. Falk and J. T. Vogelstein. NeuroData's Open Data Cloud Ecosystem. Harvard University, 2019.
  2. 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, 2019.
  3. J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. Clustering Multi-Modal Connectomes. OHBM 2019, 2019.
  4. J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. Human Structural Connectomes are Heritable. BME data science poster session, 2019.
  5. B. D. Pedigo, J. Chung, E. W. Bridgeford, B. Varjavand, C. E. Priebe, and J. T. Vogelstein. GraSPy: an Open Source Python Package for Statistical Connectomics. Max Planck /HHMI Connectomics Meeting Berlin, 2019.
  6. A. Baden, E. Perlman, F. Collman, S. Smith, J. T. Vogelstein, and R. Burns. Processing and Analyzing Terascale Conjugate Array Tomography Data. Berlin, 2017.
  7. E. Perlman. NEURODATA: ENABLING BIG DATA NEUROSCIENCE. Kavli, 2017.
  8. S. Chen, K. Liu, Y. Yuguang, L. Seonjoo, M. Lindquist, B. Caffo, and J. T. Vogelstein. A Sparse High Dimensional State-Space Model with an Application to Neuroimaging Data. Figshare, 2015.
  9. S. Chen, J. T. Vogelstein, S. Lee, M. Lindquist, and B. Caffo. High Dimensional State Space Model with L-1 and L-2 Penalties. ENAR 2015, 2015.
  10. F. Collman, R. Serafin, S. Davis, O. Gliko, T. M. Keenan, K. Parker, O. E. Linnaea, and S. J. Smith. An integrated imaging and staining platform for cubic millimeter scale array tomography. Society for Neuroscience, 2015.
  11. E. L. Deyer, H. L. Fernandes, W. G. Roncal, D. Gursoy, J. T. Vogelstein, X. Xiao, C. Jacobsen, K. P. Kording, and N. Kasthuri. X-Brain: Quantifying Mesoscale Neuroanatomy Using X-ray Microtomography. Figshare, 2015.
  12. S. J. Smith, R. Burns, M. Chevillet, E. Lein, G. Sapiro, W. Seeley, J. Trimmer, J. T. Vogelstein, and R. Weinberg. The Open Synaptome Project: Toward a Microscopy-Based Platform for Single-synapse Analysis of Diverse Populations of CNS Synapses. Society for Neuroscience, 2015.
  13. J. T. Vogelstein. Open Connectome Project & NeuroData: Enabling Data-Driven Neuroscience at Scale. Society for Neuroscience, 2015.
  14. S. Wang, Z. Yang, X. Zuo, M. Milham, C. Craddock, C. E. Priebe, and J. T. Vogelstein. Optimal Design for Discovery Science: Applications in Neuroimaging. Figshare, 2015.
  15. S. Sikka, B. Cheung, R. Khanuja, S. Ghosh, C. Yan, Q. Li, J. T. Vogelstein, R. Burns, S. Colcombe, C. Craddock, M. Maarten, C. Kelly, A. Dimartino, F. Castellanos, and M. Milham. Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). 5th INCF Congress of Neuroinformatics, Munich, Germany, 2014.
  16. R. D. Airan, J. T. Vogelstein, B. Caffo, J. J. Pekar, and H. I. Sair. Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI. Proc ISMRM, 2013.
  17. W. R. Gray, D. M. Kleissas, J. M. Burck, P. Manavalan, J. T. Vogelstein, E. Perlman, R. Burns, and J. R. Vogelstein. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. OHBM, 2013.
  18. D. Koutra, Y. Gong, S. Ryman, R. Jung, J. T. Vogelstein, and C. Faloutsos. Are All Brains Wired Equally? Proceedings of the 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2013.
  19. D. Mhembere, R. Burns, J. T. Vogelstein, and J. R. Vogelstein. Multivariate Invariants from Massive Brain-Graphs. OHBM, 2013.
  20. E. A. Pnevmatikakis, T. Machado, L. Grosenick, B. Poole, J. T. Vogelstein, and L. Paninski. Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium imaging data. COSYNE, 2013.
  21. Y. Qin, D. Mhembere, S. Ryman, R. Jung, J. Vogelstein, R. Burns, J. T. Vogelstein, and C. E. Priebe. Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood. OHBM, 2013.
  22. S. Sikka, B. Cheung, R. Khanuja, S. Ghosh, C. Yan, Q. Li, J. T. Vogelstein, R. Burns, S. Colcombe, C. Craddock, M. Maarten, C. Kelly, A. Dimartino, F. Castellanos, and M. Milham. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes. OHBM, 2013.
  23. N. Sismanis, D. L. Sussman, J. T. Vogelstein, W. Gray, R. J. Vogelstein, E. Perrlman, D. Mhembere, S. Ryman, R. Jung, R. Burns, C. Priebe, N. Pitsianis, and X. Sun. Feature Clustering from a Brain Graph for Voxel-to-Region Classification. 5th Panhellic Conference on Biomedical Technology, 2013.
  24. D. Sussman, D. Mhembere, S. Ryman, R. Jung, J. R. Vogelstein, R. Burns, J. T. Vogelstein, and C. Priebe. Massive Diffusion MRI Graph Structure Preserves Spatial Information. OHBM, 2013.
  25. J. T. Vogelstein and C. E. Priebe. Nonparametric Two-Sample Testing on Graph-Valued Data. Duke Workshop on Sensing and Analysis of HighDimensional Data, 2013.
  26. W. R. Gray, D. M. Kleissas, J. M. Burck, J. T. Vogelstein, E. Perlman, P. M. Burlina, R. Burns, and J. R. Vogelstein. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. Cold Spring Harbor Laboratory, Neuronal Circuits, 2012.
  27. J. T. Vogelstein, S. Sikka, B. Cheung, R. Khanuja, Q. Li, C. G. Yan, C. Priebe, V. Calhoun, R. J. Vogelstein, M. Milham, and R. Burns. BRAINSTORM towards clinically and scientifically useful neuroimaging analytics. Neuroinformatics, 2012.
  28. J. T. Vogelstein, D. Bock, W. R. Gray, D. Sussman, R. Burns, D. Kleissas, D. Marchette, D. E. Fishkind, M. Tang, G. Hager, J. R. Vogelstein, and C. E. Priebe. Statistical Connectomics. Janelia Farm conference, Statistical Inference and Neuroscience, 2012.
  29. W. R. Gray, J. A. Bogovic, J. T. Vogelstein, C. Ye, B. A. Landman, J. L. Prince, and R. J. Vogelstein. Magnetic resonance connectome automated pipeline and repeatability analysis. Society for Neuroscience, 2011.
  30. J. T. Vogelstein, D. E. Fishkind, D. L. Sussman, and C. E. Priebe. Large graph classification: theory and statistical connectomics applications. IMA conference on Large Graphs, 2011.
  31. J. T. Vogelstein, W. Gray, J. G. Martin, G. C. Coppersmith, M. Dredze, J. Bogovic, J. L. Prince, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. Connectome Classification using statistical graph theory and machine learning. Society for Neuroscience, 2011.
  32. J. T. Vogelstein, W. R. Gray, R. J. Vogelstein, J. Bogovic, S. Resnick, J. Prince, and C. E. Priebe. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. Organization for Human Brain Mapping, 2011.
  33. J. T. Vogelstein, E. Perlman, D. Bock, W. C. Lee, M. Chang, B. Kasthuri, M. Kazhdan, C. Reid, J. Lichtman, R. Burns, and R. J. Vogelstein. Open Connectome Project: collectively reverse engineering the brain one synapse at a time. None 2011.
  34. J. T. Vogelstein, D. L. Sussman, M. Tang, D. E. Fishkind, and C. E. Priebe. Dot product embedding in large (errorfully observed) graphs with applications in statistical connectomics. IMA conference on Large Graphs, 2011.
  35. W. R. Gray, J. T. Vogelstein, J. Bogovic, A. Carass, J. L. Prince, B. Landman, D. Pham, L. Ferrucci, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. DARPA Neural Engineering, Science and Technology Forum, 2010.
  36. J. T. Vogelstein, J. Bogovic, A. Carass, W. Gray, J. Prince, B. Landman, D. Pham, L. Ferrucci, S. Resnick, C. E. Priebe, and R. Vogelstein. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. Organization for Human Brain Mapping, 2010.
  37. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. COSYNE, 2010.
  38. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. COSYNE, 2010.
  39. J. T. Vogelstein, C. E. Priebe, R. Burns, R. J. Vogelstein, and J. Lichtman. Measuring and reconstructing the brain at the synaptic scale: towards a biofidelic human brain in silico. DARPA Neural Engineeering, Science and Technology Forum, 2010.
  40. J. T. Vogelstein, R. Vogelstein, and C. E. Priebe. A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains. CSHL conference on Neural Circuits, 2010.
  41. J. T. Vogelstein, Y. Mishchchenko, A. M. Packer, T. A. Machado, R. Yuste, and L. Paninski. Towards Confirming Neural Circuits from Population Calcium Imaging. NIPS Workshop on Workshop on Connectivity Infernence in Neuroimaging, 2009.
  42. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. COSYNE, 2009.
  43. J. T. B. Vogelstein and L. Paninski. Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging. COSYNE, 2008.
  44. J. T. Vogelstein, B. Babadi, B. Watson, R. Yuste, and L. Paninski. From Calcium Sensitive Fluorescence Movies to Spike Trains. Society for Neuroscience, 2008.
  45. J. T. Vogelstein and L. Paninski. Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging. Integrative Approaches to Brain Complexity, 2008.
  46. J. T. Vogelstein, B. Jedynak, K. Zhang, and L. Paninski. Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging. Society for Neuroscience, 2007.
  47. J. T. Vogelstein, K. Zhang, B. Jedynak, and L. Paninski. Maximum Likelihood Inference of Neural Dynamics under Noisy and Intermittent Observations using Sequential Monnte Carlo EM Algorithms. COSYNE, 2007.
  48. J. T. Vogelstein and K. Zhang. A novel theory for simultaneous representation of multiple dynamic states in hippocampus. Society for Neuroscience, 2004.
  49. J. T. Vogelstein, L. Snyder, M. Warchol, and D. Angelaki. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. Society for Neuroscience, 2002.
  50. J. T. Vogelstein, L. Snyder, M. Warchol, and D. Angelaki. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. Society for Neuroscience, 2002.