Presentations

Talks
  1. J. T. Vogelstein. Connectome Coding. Society for Neuroscience, 2018.
  2. J. T. Vogelstein. NeuroData: A Community-developed open-source computational ecosystem for big neuro data. NeuroNex, 2018.
  3. J. T. Vogelstein. Multiscale Graph Correlation: A Knowledge Representation System for Discovering Latent Geometric Structure. DARPA SIMPLEX PI Review Meeting, 2018.
  4. J. T. Vogelstein. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data. Princeton, 2018.
  5. E. W. Bridgeford. A High-Throughput Pipeline Identifies Robust Connectomes but Troublesome Variability. Organization of Human Brain Mapping, 2018.
  6. E. Perlman. NeuroData: Embracing Open Source for Big Data Neuroscience. NSF NeuroNex Workshop on Super 3DEM, 2018.
  7. J. T. Vogelstein and V. Chandrashekhar. NeuroNex + Stanford. NeuroNex-Stanford, 2018.
  8. G. Kiar. Connectome Coding: what is it, how do we do it, and why do we care? Data science in Neuroscience Symposium, 2018.
  9. J. T. Vogelstein. Data Intensive Brain Science. Kavli Neuroscience Discovery Institute, 2018.
  10. J. T. Vogelstein. Lifelong Learning Forests. Darpa L2M PI Meeting, 2018.
  11. J. T. Vogelstein. Using Big Data Science to Understand What Goes On in our Heads. SOHOP Faculty Spotlight, 2018.
  12. J. T. Vogelstein. Engineering the Future of Medicine: Data Intensive Biomedical Science. Johns Hopkins University Biomedical Engineering, 2018.
  13. G. Kiar. A Data Driven Approach for Tackling Big Data Connectomics. Feindel Brain Imaging Lecture, 2018.
  14. J. T. Vogelstein. Discovering Relationships and their Geometry Across Disparate Data Modalities. Yale, 2018.
  15. G. Kiar. Science in the Cloud (SIC): A use-case in MRI Connectomics. Open Science Special Interest Group, 2017.
  16. Y. Lee. Network Dependence Testing via Diffusion Maps and Distance-Based Correlations. Joint Statistical Meetings, 2017.
  17. D. Mhembere. knor: K-means NUMA Optimized Routines Library. High-Performance Parallel and Distributed Computing, 2017.
  18. T. M. Tomita. ROFLMAO: Robust Oblique Forests with Linear Matrix Operations. SIAM International Conference on Data Mining 2017, 2017.
  19. J. T. Vogelstein. Challenges and Opportunities in Big Data for Neuroscientists. Society for Neuroscience: DC Metro Area Chapter Keynote Address, 2017.
  20. J. T. Vogelstein. Using Big Data Science to Understand What Goes on in Our Heads. SOHOP Faculty Spotlight, 2017.
  21. J. T. Vogelstein. NeuroData. 2017.
  22. J. T. Vogelstein. The International Brain Station (TIBS). JHU BME and Tsinghua University, 2017.
  23. J. T. Vogelstein. Opportunities and Challenges in Big Data Neuroscience. Society for Neuroscience, 2017.
  24. J. T. Vogelstein. Connectome Coding. Schmidt Sciences, 2017.
  25. J. T. Vogelstein. NeuroStorm. Global Brain Workshop 2 JHU, 2017.
  26. J. T. Vogelstein. NeuroData: Enabling Terascale Neuroscience for Everyone. Keystone Symposia: State of the Brain, 2016.
  27. J. T. Vogelstein. Using Big Data Science to Understand What Goes on in Our Heads. SOHOP Faculty Spotlight, 2016.
  28. J. T. Vogelstein. Global Brain Workshop 2016. Global Brain Workshop NSF+JHU at Kavli, 2016.
  29. J. T. Vogelstein. The International Brain Station (TIBS). United Nations Global Brain Workshop Meeting, 2016.
  30. J. T. Vogelstein. NeuroData:Enabling Terascale Neuroscience. JHU Kavli Neuroscience Discovery Institute, 2016.
  31. J. T. Vogelstein. The International Brain Station (TIBS). Kavli Foundation, 2016.
  32. J. T. Vogelstein. NeuroData 2016. NeuroData Lab Retreat, 2016.
  33. J. T. Vogelstein. Global Brain Workshop 2106. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  34. J. T. Vogelstein. NeuroData:Enabling Terascale Neuroscience. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  35. J. T. Vogelstein. Learning a Data-Driven Nosology:Progress, Challenges & Opportunities. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  36. J. T. Vogelstein, M. I. Miller and R. Hunganir. Global Brain Workshop 2016. Kavli Institute for Neuroscience Discovery Center for Imaging Science @ JHU, 2016.
  37. J. T. Vogelstein. Opportunities and Challenges in Big Data Neuroscience. DoE, 2015.
  38. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data. SIMPLEX Kickoff, 2015.
  39. J. T. Vogelstein. Law of Large Graphs. DARPA Graphs, 2015.
  40. J. T. Vogelstein. Open Connectome Project: Lowering the Barrier to Entry of Big Data Neuroscience. Institute for Computational Medicine at Johns Hopkins University, 2015.
  41. J. T. Vogelstein. Special Symposium: Neuroscience in the 21st Century. Kavli, 2015.
  42. J. T. Vogelstein. Research Computing Support for Neuroscience and Other Life Sciences. CASC, 2015.
  43. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 1. DARPA SIMPLEX PI Meeting, 2015.
  44. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 2. DARPA SIMPLEX PI Meeting, 2015.
  45. J. T. Vogelstein and L. Paninski. Spike inference from calcium imaging using sequential Monte Carlo methods. AMSI Program on Sequential Monte Carlo, 2015.
  46. J. T. Vogelstein. Open Source Platform for Heterogenous Brain Data. figshare, 2015.
  47. J. T. Vogelstein. big time (series data in neuroscience). figshare, 2015.
  48. J. T. Vogelstein. Top Challenges of Big Data Neuroscience. BRAIN Initiative Workshop, 2014.
  49. J. T. Vogelstein. Big Statistics for Brain Sciences. Baylor College of Medicine, Department of Neuroscience, 2014.
  50. J. T. Vogelstein. Open-Science Platform for Heterogeneous Brain Data: Opportunities and Challenges. Kavli, 2014.
  51. J. T. Vogelstein. Big (Neuro) Statistics. Kavli Salon, 2014.
  52. J. T. Vogelstein. Statistical Models and Inference for big Brain-Graphs. NIPS Workshop on Acquiring and analyzing the activity of large neural ensembles, 2013.
  53. J. T. Vogelstein. Statistical Inference on Graphs. University of Michigan, 2013.
  54. J. T. Vogelstein. Statistical Inference on Graphs. Scientific Computing Institute, University of Utah, 2013.
  55. J. T. Vogelstein. Open Problems in Neuropsychiatry. Data Seminar, Duke University, 2013.
  56. J. T. Vogelstein. Beyond Little Neuroscience. Beyond Optogenetics workshop at Cosyne, 2013.
  57. J. T. Vogelstein. Decision Theoretic Approach to Statistical Inference. guest Lecture in Current Topics in Machine Learning, Johns Hopkins University, 2012.
  58. J. T. Vogelstein. BIG NEURO. Theory and Neurobiology, Duke University, 2012.
  59. J. T. Vogelstein. Open Connectome Project. Academic Medical Center, Amsterdam, 2012.
  60. J. T. Vogelstein. Consistent Graph Classification. Guest Lecture in Deisseroth Lab, Stanford University, 2011.
  61. J. T. Vogelstein. Statistical Connectomics. Harvard University Connectomics Labs, 2011.
  62. J. T. Vogelstein. What can Translational neuroimaging Research do for Clinical Practice. Child Mind Institute, 2011.
  63. J. T. Vogelstein. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. Organization for Human Brain Mapping, 2011.
  64. J. T. Vogelstein. Consistent Connectome Classification. Math/Bio Seminar, Duke University, 2011.
  65. J. T. Vogelstein. Once we get connectomes, what the \%\#* are we going to do with them? Krasnow Institute for Advanced Study at George Mason Univeristy, 2011.
  66. J. T. Vogelstein. Once we get connectomes, what the \%\#* are we going to do with them? Institute of Neuroinformatics, 2011.
  67. J. T. Vogelstein. Are mental properties supervenient on brain properties. 2011.
  68. J. T. Vogelstein. Towards Inference and Analaysis of Neural Circuits Inferred from Population Calcium Imaging. Guest Lecture in Schnitzer Lab, 2009.
  69. J. T. Vogelstein. Sequential Monte Carlo in Neuroscience. SAMSI Program on Sequential Monte Carlo, Tracking Working Group, 2009.
  70. J. T. Vogelstein. OOPSI: A Family of Optimal OPtical Spike Inference Algorithms for Inferring Neural Connectivity from Population Calcium Imaging. Dissertation Defense, 2009.
  71. J. T. Vogelstein. Towards Inferring Neural Circuits from Calcium Imaging. Guest Lecture in Yuste Lab, 2009.
  72. J. T. Vogelstein. Neurocognitive Graph Theory. national Security Agency, 2009.
  73. J. T. Vogelstein. Inferring spike trains from Calcium Imaging. Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 2008.
  74. J. T. Vogelstein. Inferring spike times given typical time-series fluorescence observations. Department of Applied Mathematics and Statistics, Johns Hopkins University, 2008.
  75. J. T. Vogelstein. Inferring spike trains from Calcium Imaging. Cambridge University, Gatsby Unit, and University College London, 2008.
  76. J. T. Vogelstein. Inferring Spike Trains Given Calcium-Sensitive Fluorescence Observations. Statistical Analysis of Neural Data, 2008.
  77. J. T. Vogelstein. Model based optimal inference of spike times and calcium dynamics givern noisy and intermittent calcium-fluorescence observations. Neurotheory Center of Columbia University, 2007.
Posters
  1. S. Chen et al. A Sparse High Dimensional State-Space Model with an Application to Neuroimaging Data. 2015.
  2. S. Chen et al. High Dimensional State Space Model with L-1 and L-2 Penalties. 2015.
  3. F. Collman et al. An integrated imaging and staining platform for cubic millimeter scale array tomography. 2015.
  4. E. L. Deyer et al. X-Brain: Quantifying Mesoscale Neuroanatomy Using X-ray Microtomography. 2015.
  5. S. J. Smith et al. The Open Synaptome Project: Toward a Microscopy-Based Platform for Single-synapse Analysis of Diverse Populations of CNS Synapses. 2015.
  6. J. T. Vogelstein. Open Connectome Project & NeuroData: Enabling Data-Driven Neuroscience at Scale. 2015.
  7. S. Wang et al. Optimal Design for Discovery Science: Applications in Neuroimaging. 2015.
  8. R. D. Airan, J. T. Vogelstein and others. Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI. 2013.
  9. C. Craddock and others. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes. 2013.
  10. W. R. Gray and others. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. 2013.
  11. D. Koutra et al. Are All Brains Wired Equally? 2013.
  12. D. Mhembere and others. Multivariate Invariants from Massive Brain-Graphs. 2013.
  13. E. A. Pnevmatikakis and others. Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium inaging data. 2013.
  14. Y. Qin and others. Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood. 2013.
  15. N. Sismanis and others. Feature Clustering from a Brain Graph for Voxel-to-Region Classification. 2013.
  16. D. Sussman and others. Massive Diffusion MRI Graph Structure Preserves Spatial Information. 2013.
  17. J. T. Vogelstein and C. E. Priebe. Nonparametric Two-Sample Testing on Graph-Valued Data. 2013.
  18. J. T. Vogelstein and others. Anomaly Screening and Clustering of Multi-OBject Movies via Multiscale Structure Learning. 2013.
  19. W. R. Gray and others. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. 2012.
  20. J. T. Vogelstein and others. Statistical Connectomics. 2012.
  21. J. T. Vogelstein and others. BRAINSTORM towards clinically and scientifically useful neuroimaging analytics. 2012.
  22. W. R. Gray et al. Magnetic resonance connectome automated pipeline and repeatability analysis. 2011.
  23. J. T. Vogelstein et al. Large graph classification: theory and statistical connectomics applications. 2011.
  24. J. T. Vogelstein et al. Connectome Classification using statistical graph theory and machine learning. 2011.
  25. J. T. Vogelstein et al. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. 2011.
  26. J. T. Vogelstein et al. Open Connectome Project: collectively reverse engineering the brain one synapse at a time. 2011.
  27. J. T. Vogelstein et al. Dot product embedding in large (errorfully observed) graphs with applications in statistical connectomics. 2011.
  28. W. R. Gray et al. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. 2010.
  29. J. T. Vogelstein et al. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. 2010.
  30. J. T. Vogelstein et al. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. 2010.
  31. J. T. Vogelstein et al. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. 2010.
  32. J. T. Vogelstein et al. Measuring and reconstructing the brain at the synaptic scale: towards a biofidelic human brain in silico. 2010.
  33. J. T. Vogelstein, R. Vogelstein and C. E. Priebe. A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains. 2010.
  34. J. T. Vogelstein et al. Towards Confirming Neural Circuits from Population Calcium Imaging. 2009.
  35. J. T. Vogelstein et al. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. 2009.
  36. J. T. B. Vogelstein and L. Paninski. Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging. 2008.
  37. J. T. Vogelstein et al. From Calcium Sensitive Fluorescence Movies to Spike Trains. 2008.
  38. J. T. Vogelstein and L. Paninski. Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging. 2008.
  39. J. T. Vogelstein et al. Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging. 2007.
  40. J. T. Vogelstein et al. Maximum Likelihood Inference of Neural Dynamics under Noisy and Intermittent Observations using Sequential Monnte Carlo EM Algorithms. 2007.
  41. J. T. Vogelstein and K. Zhang. A novel theory for simultaneous representation of multiple dynamic states in hippocampus. 2004.
  42. J. T. Vogelstein et al. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. 2002.
  43. J. T. Vogelstein et al. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. 2002.