Talks

Invited
  1. J. Chung. Statistical Methods for Population of Connectomes. Organization of Human Brain Mapping, 2019.
  2. J. T. Vogelstein. Open Access to the Brain: a Computer "Connectome" Links Brain Images in Fine Detail. JHM Boot Camp, 2019.
  3. J. T. Vogelstein. Statistical Foundations For Connectomics. Max Planck / HHMI Connectomics Meeting, 2019.
  4. J. T. Vogelstein. Big Biomedical Data Science. Sol Goldman International Conference, 2019.
  5. J. T. Vogelstein. Connectal Coding. Dipy Workshop, 2019.
  6. J. T. Vogelstein. Connectome Coding. Society for Neuroscience, 2018.
  7. J. T. Vogelstein. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data. Princeton, 2018.
  8. E. W. Bridgeford. A High-Throughput Pipeline Identifies Robust Connectomes but Troublesome Variability. Organization of Human Brain Mapping, 2018.
  9. E. Perlman. NeuroData: Embracing Open Source for Big Data Neuroscience. NSF NeuroNex Workshop on Super 3DEM, 2018.
  10. J. T. Vogelstein. Using Big Data Science to Understand What Goes On in our Heads. SOHOP Faculty Spotlight, 2018.
  11. J. T. Vogelstein. Discovering Relationships and their Geometry Across Disparate Data Modalities. Yale, 2018.
  12. J. T. Vogelstein. Discovering Relationships and their Geometry Across Disparate Data Modalities. Stanford, 2017.
  13. J. T. Vogelstein. Using Big Data Science to Understand What Goes on in Our Heads. SOHOP Faculty Spotlight, 2017.
  14. J. T. Vogelstein. Challenges and Opportunities in Big Data for Neuroscientists. Society for Neuroscience: DC Metro Area Chapter Keynote Address, 2017.
  15. J. T. Vogelstein. Opportunities and Challenges in Big Data Neuroscience. Society for Neuroscience, 2017.
  16. J. T. Vogelstein. Using Big Data Science to Understand What Goes on in Our Heads. SOHOP Faculty Spotlight, 2016.
  17. J. T. Vogelstein. NeuroData: Enabling Terascale Neuroscience for Everyone. Keystone Symposia: State of the Brain, 2016.
  18. J. T. Vogelstein. The International Brain Station (TIBS). Kavli Foundation, 2016.
  19. J. T. Vogelstein. The International Brain Station (TIBS). United Nations Global Brain Workshop Meeting, 2016.
  20. J. T. Vogelstein and L. Paninski. Spike inference from calcium imaging using sequential Monte Carlo methods. AMSI Program on Sequential Monte Carlo, 2015.
  21. J. T. Vogelstein. Top Challenges of Big Data Neuroscience. BRAIN Initiative Workshop, 2014.
  22. J. T. Vogelstein. Big (Neuro) Statistics. Kavli Salon, 2014.
  23. J. T. Vogelstein. Open-Science Platform for Heterogeneous Brain Data: Opportunities and Challenges. Kavli, 2014.
  24. J. T. Vogelstein. Beyond Little Neuroscience. Beyond Optogenetics workshop at Cosyne, 2013.
  25. J. T. Vogelstein. Statistical Inference on Graphs. University of Michigan, 2013.
  26. J. T. Vogelstein. Statistical Inference on Graphs. Scientific Computing Institute, University of Utah, 2013.
  27. J. T. Vogelstein. BIG NEURO. Theory and Neurobiology, Duke University, 2012.
  28. J. T. Vogelstein. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. Organization for Human Brain Mapping, 2011.
  29. J. T. Vogelstein. Consistent Connectome Classification. Math/Bio Seminar, Duke University, 2011.
  30. 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.
  31. J. T. Vogelstein. Once we get connectomes, what the \%\#* are we going to do with them? Institute of Neuroinformatics, 2011.
  32. J. T. Vogelstein. Statistical Connectomics. Harvard University Connectomics Labs, 2011.
  33. J. T. Vogelstein. What can Translational neuroimaging Research do for Clinical Practice. Child Mind Institute, 2011.
  34. J. T. Vogelstein. Inferring Spike Trains Given Calcium-Sensitive Fluorescence Observations. Statistical Analysis of Neural Data, 2008.
  35. J. T. Vogelstein. Inferring spike trains from Calcium Imaging. Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 2008.
  36. J. T. Vogelstein. Inferring spike trains from Calcium Imaging. Cambridge University, Gatsby Unit, and University College London, 2008.
  37. 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.
Other
  1. J. T. Vogelstein. Ailey in an Hour: (A "Soup-to-Nuts" Pipeline for Analysis of Whole Cleared Brain Data). NeuroNex, 2019.
  2. J. T. Vogelstein, H. Helm, R. Mehta, C. E. Priebe, and R. Arora. A Theory and Practice of the Lifelong Learnable. L2M, 2019.
  3. J. T. Vogelstein and R. Burns. Data Science Core. Harvard University, 2019.
  4. J. Browne. Forest Packing: Fast Parallel, Decision Forests. SIAM International Conference on Data Mining, 2019.
  5. D. Tward. Brain mapping tools for neuroscience research. NeuroNex, 2019.
  6. J. T. Vogelstein. Big Data and the Life Sciences. Sloan Foundation, 2019.
  7. J. T. Vogelstein. Journey to Here. JHU BMES talks, 2019.
  8. J. T. Vogelstein. NeuroData (Science). Kavli, 2019.
  9. J. T. Vogelstein. Lifelong Learning Forests. L2M, 2019.
  10. J. T. Vogelstein. NeuroData Tools. NeuroData Hackashop, 2019.
  11. J. T. Vogelstein. Biomedical Big Data and Data Science. JHU BME, 2019.
  12. J. T. Vogelstein. NeuroData: A Community-developed open-source computational ecosystem for big neuro data. NeuroNex, 2018.
  13. C. Shen. The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. Joint Statistical Meeting, 2018.
  14. J. T. Vogelstein. Multiscale Graph Correlation: A Knowledge Representation System for Discovering Latent Geometric Structure. DARPA SIMPLEX PI Review Meeting, 2018.
  15. J. T. Vogelstein and V. Chandrashekhar. NeuroNex + Stanford. NeuroNex-Stanford, 2018.
  16. G. Kiar. Connectome Coding: what is it, how do we do it, and why do we care? Data science in Neuroscience Symposium, 2018.
  17. J. T. Vogelstein. Data Intensive Brain Science. Kavli Neuroscience Discovery Institute, 2018.
  18. J. T. Vogelstein. Lifelong Learning Forests. Darpa L2M PI Meeting, 2018.
  19. J. T. Vogelstein. Engineering the Future of Medicine: Data Intensive Biomedical Science. Johns Hopkins University Biomedical Engineering, 2018.
  20. D. Mhembere. knor: a NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means library. HPDC, 2017.
  21. G. Kiar. Science in the Cloud (SIC): A use-case in MRI Connectomics. Open Science Special Interest Group, 2017.
  22. Y. Lee. Network Dependence Testing via Diffusion Maps and Distance-Based Correlations. Joint Statistical Meetings, 2017.
  23. D. Mhembere. knor: K-means NUMA Optimized Routines Library. High-Performance Parallel and Distributed Computing, 2017.
  24. T. M. Tomita. ROFLMAO: Robust Oblique Forests with Linear Matrix Operations. SIAM International Conference on Data Mining 2017, 2017.
  25. J. T. Vogelstein. Challenges and Opportunities in Big Data for Neuroscientists. Society for Neuroscience: DC Metro Area Chapter Keynote Address, 2017.
  26. J. T. Vogelstein. NeuroData. 2017.
  27. J. T. Vogelstein. The International Brain Station (TIBS). JHU BME and Tsinghua University, 2017.
  28. J. T. Vogelstein. Connectome Coding. Schmidt Sciences, 2017.
  29. J. T. Vogelstein. NeuroStorm. Global Brain Workshop 2 JHU, 2017.
  30. C. Shen. Multiscale Generalized Correlation. Joint Statistical Meeting, 2016.
  31. J. T. Vogelstein. NeuroData:Enabling Terascale Neuroscience. JHU Kavli Neuroscience Discovery Institute, 2016.
  32. J. T. Vogelstein. The International Brain Station (TIBS). Kavli Foundation, 2016.
  33. J. T. Vogelstein. NeuroData 2016. NeuroData Lab Retreat, 2016.
  34. J. T. Vogelstein. Global Brain Workshop 2016. Global Brain Workshop NSF+JHU at Kavli, 2016.
  35. J. T. Vogelstein. Global Brain Workshop 2016. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  36. J. T. Vogelstein. NeuroData:Enabling Terascale Neuroscience. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  37. J. T. Vogelstein. Learning a Data-Driven Nosology:Progress, Challenges & Opportunities. Kavli Neuroscience Discovery Institute & Center for Imaging Science, 2016.
  38. J. T. Vogelstein, M. I. Miller, and R. Hunganir. Global Brain Workshop 2016. Kavli Neuroscience Discovery Institute & Center for Imaging Science @ JHU, 2016.
  39. C. Shen. Local Distance Correlation for Testing Independence. Temple University, 2015.
  40. J. T. Vogelstein. Research Computing Support for Neuroscience and Other Life Sciences. CASC, 2015.
  41. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data. SIMPLEX Kickoff, 2015.
  42. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 1. DARPA SIMPLEX PI Meeting, 2015.
  43. J. T. Vogelstein. From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 2. DARPA SIMPLEX PI Meeting, 2015.
  44. J. T. Vogelstein. Special Symposium: Neuroscience in the 21st Century. Kavli, 2015.
  45. J. T. Vogelstein. Law of Large Graphs. DARPA Graphs, 2015.
  46. J. T. Vogelstein. Open Connectome Project: Lowering the Barrier to Entry of Big Data Neuroscience. Institute for Computational Medicine at Johns Hopkins University, 2015.
  47. J. T. Vogelstein. Opportunities and Challenges in Big Data Neuroscience. DoE, 2015.
  48. J. T. Vogelstein. big time (series data in neuroscience). figshare, 2015.
  49. J. T. Vogelstein. Open Source Platform for Heterogenous Brain Data. figshare, 2015.
  50. J. T. Vogelstein. Big Statistics for Brain Sciences. Baylor College of Medicine, Department of Neuroscience, 2014.
  51. J. T. Vogelstein. Big (Neuro) Statistics. Kavli Salon, 2014.
  52. J. T. Vogelstein. Open Problems in Neuropsychiatry. Data Seminar, Duke University, 2013.
  53. J. T. Vogelstein. Statistical Models and Inference for big Brain-Graphs. NIPS Workshop on Acquiring and analyzing the activity of large neural ensembles, 2013.
  54. J. T. Vogelstein. Decision Theoretic Approach to Statistical Inference. guest Lecture in Current Topics in Machine Learning, Johns Hopkins University, 2012.
  55. J. T. Vogelstein. Open Connectome Project. Academic Medical Center, Amsterdam, 2012.
  56. J. T. Vogelstein. Are mental properties supervenient on brain properties. None 2011.
  57. J. T. Vogelstein. Consistent Graph Classification. Guest Lecture in Deisseroth Lab, Stanford University, 2011.
  58. J. T. Vogelstein. Neurocognitive Graph Theory. National Security Agency, 2009.
  59. J. T. Vogelstein. OOPSI: A Family of Optimal OPtical Spike Inference Algorithms for Inferring Neural Connectivity from Population Calcium Imaging. Dissertation Defense, 2009.
  60. J. T. Vogelstein. Sequential Monte Carlo in Neuroscience. SAMSI Program on Sequential Monte Carlo, Tracking Working Group, 2009.
  61. J. T. Vogelstein. Towards Inference and Analaysis of Neural Circuits Inferred from Population Calcium Imaging. Guest Lecture in Schnitzer Lab, 2009.
  62. J. T. Vogelstein. Towards Inferring Neural Circuits from Calcium Imaging. Guest Lecture in Yuste Lab, 2009.
  63. J. T. Vogelstein. Inferring spike times given typical time-series fluorescence observations. Department of Applied Mathematics and Statistics, Johns Hopkins University, 2008.