Team

Joshua Vogelstein, Ph.D.

Associate Professor
Department of Biomedical Engineering
Johns Hopkins University
jovo@jhu.edu
jovo.me

Joshua Vogelstein, PhD, is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, with joint appointments in Applied Mathematics and Statistics, Computer Science, Electrical and Computer Engineering, Neuroscience, and Biostatistics. His research focuses on biomedical data science, specifically high-dimensional statistics and the intersection of artificial and natural intelligence. In addition to his academic work, he was a co-founder of software startup Gigantum, which was acquired by nVidia in early 2022; and Global Doman Partners, a quantitative hedge fund that was acquired by Mosaic Investment Partners in 2012. His research has been featured in a number of prominent scientific and engineering journals and conferences including Nature, Science, and the Proceedings of the National Academy of Sciences (PNAS); and in recognition of his many contributions, he has received the Transformative Research Award from NIH, the NSF CAREER award, and research grants from Microsoft Research, among many other for-profit and non-profit organizations.

Links

CV

Personal Information

Primary Appointment

  • 02/22 – Associate Professor Department of Biomedical Engineering, JHU.
  • 08/14 – 02/22 Assistant Professor Department of Biomedical Engineering, JHU.

Joint Appointments

  • 09/19 – Joint Appointment Department of Biostatistics, JHU, Baltimore, MD, USA.
  • 08/15 – Joint Appointment Department of Applied Mathematics and Statistics, JHU.
  • 08/14 – Joint Appointment Department of Neuroscience, JHU.
  • 08/14 – Joint Appointment Department of Computer Science, JHU.

Institutional and Center Appointments

  • 08/15 – Steering Committee Kavli Neuroscience Discovery Institute (KNDI).
  • 08/14 – Core Faculty Institute for Computational Medicine, JHU.
  • 08/14 – Core Faculty Center for Imaging Science, JHU.
  • 08/14 – Assistant Research Faculty Human Language Technology Center of Excellence, JHU.
  • 10/12 – Affiliated Faculty Institute for Data Intensive Engineering and Sciences, JHU.

Education & Training

  • 2003 – 2009 Ph.D in Neuroscience Johns Hopkins School of Medicine
    Advisor: Eric Young
    Thesis: OOPSI: a family of optical spike inference algorithms for inferring neural connectivity from population calcium imaging
  • 2009 – 2009 M.S. in Applied Mathematics & Statistics Johns Hopkins University
  • 1998 – 2002 B.A. in Biomedical Engineering Washington University, St. Louis

Academic Experience

  • 08/18 – Director of Biomedical Data Science Focus Area Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • 05/16 – Visiting Scientist Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
  • 10/12 – 08/14 Endeavor Scientist Child Mind Institute, New York, NY, USA
  • 08/12 – 08/14 Affiliated Faculty Kenan Institute for Ethics, Duke University, Durham, NC, USA
  • 08/12 –08/14 Adjunct Faculty Department of Computer Science, JHU, Baltimore, MD, USA
  • 12/09 – 01/11 Post-Doctoral Fellow Department of Applied Mathematics and Statistics, Supervised by Carey E.Priebe, JHU, Baltimore, MD, USA
    Research Statistics of populations of networks
  • 06/01 – 09/01 Research Assistant Prof. Randy O'Reilly, Dept. of Psychology, University of Colorado, Denver, CO, USA
  • 06/00 – 09/00 Clinical Engineer Johns Hopkins Hospital, JHU, Baltimore, MD, USA
  • 06/99 – 08/99 Research Assistant under Dr. Jeffrey Williams Dept. of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, USA
  • 06/98 – 08/98 Research Assistant under Professor Kathy Cho Dept. of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA

Published Peer-Reviewed Research Articles

Note: CV author in bold; Trainees are underlined,
(89 papers; top 10 cited 3,922 times; H-index 36; 12 first, 13 last, 48 middle authorships) as of 2021/08/29

Manuscripts Not Yet Accepted

  • [31]

    B. D. Pedigo, M. Winding, C. E. Priebe, and J. T. Vogelstein. "Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes" bioRxiv, 2022. URL: https://www.biorxiv.org/content/10.1101/2022.05.19.492713

  • [30]

    Jayanta Dey, Ashwin De Silva, Will LeVine, Jong Shin, Haoyin Xu, Ali Geisa, Tiffany Chu, Leyla Isik, and Joshua T. Vogelstein. "Out-of-distribution Detection Using Kernel Density Polytopes" arXiv, 2022. URL: https://arxiv.org/abs/2201.13001

  • [29]

    J. T. Vogelstein, T. Verstynen, K. P. I. Kording, J. W. Krakauer, R. O. Etienne-Cummings, C. E. Priebe, R. a. Burns, J. J. Knierim, J. B. a. Potash, L. Smirnova, P. Worley, Savonenko, Alena, I. Phillips, M. I. Miller, R. a. Vidal, A. Charles, N. J. Cowan, Bichuch, Maxim, A. Venkataraman, C. Li, N. K. Thakor, M. Albert, J. a. Xu, B. Caffo, T. G. Ratnanather, S. Roh, E. a. Yezerets, J. J. How, T. M. Tomita, Dey, Jayanta, N. Huang, J. M. Shin, Kinfu, Kaleab Alemayehu, P. Chaudhari, B. a. Baker, D. Jayaraman, E. Eaton, Platt, Michael, L. Ungar, L. Wehbe, A. a. Kepecs, O. Osuagwu, B. Brunton, Mensh, Brett, A. R. Muotri, G. Silva, Puppo, Francesca, F. Engert, E. Hillman, Brown, Julia, C. White, and W. Yang. "Prospective Learning: Back to the Future" arXiv [cs.LG], 2022. URL: https://arxiv.org/abs/2201.07372

  • [28]

    T. Xu, J. Cho, G. Kiar, E. W. Bridgeford, J. T. Vogelstein, and M. P. Milham. "A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences" bioRxiv, 2022. URL: https://www.biorxiv.org/content/10.1101/2022.01.27.478100v1

  • [27]

    Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. "Simplest Streaming Trees" arXiv, 2021. URL: https://arxiv.org/abs/2110.08483

  • [26]

    Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, and Carey E. Priebe. "When are Deep Networks really better than Decision Forests at small sample sizes, and how?" arXiv, 2021. URL: https://arxiv.org/abs/2108.13637

  • [25]

    R. Xiong, A. Koenecke, M. Powell, Z. Shen, J. T. Vogelstein, and S. Athey. "Federated Causal Inference in Heterogeneous Observational Data" arXiv, 2021. URL: https://arxiv.org/abs/2107.11732

  • [24]

    Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, and Joshua T. Vogelstein. "Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities" arXiv, 2021. URL: https://arxiv.org/abs/1907.00325

  • [23]

    S. Li, T. Jun, Z. Wang, Y. Kao, E. Schadt, M. F. Konig, C. Bettegowda, J. T. Vogelstein, N. Papadopoulos, R. E. Parsons, and others. "COVID-19 outcomes among hospitalized men with or without exposure to alpha-1-adrenergic receptor blocking agents" medRxiv, 2021. URL: https://www.medrxiv.org/content/10.1101/2021.04.08.21255148v1.full

  • [22]

    S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, R. Mehta, C. Shen, and J. T. Vogelstein. "hyppo: A Multivariate Hypothesis Testing Python Package" arXiv, 2021. URL: https://arxiv.org/abs/1907.02088

  • [21]

    S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein. "Nonpar MANOVA via Independence Testing" arXiv, 2021. URL: https://arxiv.org/abs/1910.08883

  • [20]

    Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, and Joshua T. Vogelstein. "Towards a theory of out-of-distribution learning" arXiv, 2021. URL: https://arxiv.org/abs/2109.14501

  • [19]

    Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, and Joshua T. Vogelstein. "Graph Matching via Optimal Transport" arXiv, 2021. URL: https://arxiv.org/abs/2111.05366

  • [18]

    T. L. Athey, T. Liu, B. D. Pedigo, and J. T. Vogelstein. "AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python" arxiv, 2021. URL: https://arxiv.org/abs/1909.02688

  • [17]

    Jaewon Chung, Bijan Varjavand, Jesus Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua T. Vogelstein, and Carey E. Priebe. "Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics" arXiv, 2021. URL: https://arxiv.org/abs/1911.02741

  • [16]

    V. Gopalakrishnan, J. Chung, E. Bridgeford, B. D. Pedigo, J. Arroyo, L. Upchurch, G. A. Johnsom, N. Wang, Y. Park, C. E. Priebe, and J. T. Vogelstein. "Multiscale Comparative Connectomics" arXiv, 2020. URL: https://arxiv.org/abs/2011.14990

  • [15]

    Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, and Carey E. Priebe. "Multiple Network Embedding for Anomaly Detection in Time Series of Graphs" arXiv, 2020. URL: https://arxiv.org/abs/2008.10055

  • [14]

    C. Shen, S. Panda, and J. T. Vogelstein. "Learning Interpretable Characteristic Kernels via Decision Forests" arXiv, 2020. URL: https://arxiv.org/abs/1812.00029

  • [13]

    K. Mehta, R. F. Goldin, D. Marchette, J. T. Vogelstein, C. E. Priebe, and G. A. Ascoli. "Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding" bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.06.18.160259 URL: https://www.biorxiv.org/content/early/2020/06/20/2020.06.18.160259

  • [12]

    Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, and Carey E. Priebe. "Omnidirectional Transfer for Quasilinear Lifelong Learning" arXiv, 2020. URL: https://arxiv.org/abs/2004.12908

  • [11]

    M. Madhyastha, K. Lillaney, J. Browne, J. Vogelstein, and R. Burns. "PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment" arXiv, 2020. URL: https://arxiv.org/abs/2011.05383

  • [10]

    C. Shen. "High-dimensional independence testing and maximum marginal correlation" arXiv, 2020. URL: https://arxiv.org/abs/2001.01095

  • [9]

    Tyler M. Tomita and Joshua T. Vogelstein. "Robust Similarity and Distance Learning via Decision Forests" arXiv, 2020. URL: https://arxiv.org/abs/2007.13843

  • [8]

    E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, T. Xu, C. Craddock, G. Kiar, W. Gray-Roncal, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. "Optimal Experimental Design for Big Data: Applications in Brain Imaging" bioRxiv, 2019. URL: https://doi.org/10.1101/802629

  • [7]

    R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. "A Consistent Independence Test for Multivariate Time-Series" arxiv, 2019. URL: https://arxiv.org/abs/1908.06486

  • [6]

    R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. "Manifold Forests: Closing the Gap on Neural Networks" arXiv, 2019. URL: https://arxiv.org/abs/1909.11799

  • [5]

    J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. "Graph Independence Testing" arXiv, 2019. URL: https://arxiv.org/abs/1906.03661

  • [4]

    H. Patsolic, S. Adali, J. T. Vogelstein, Y. Park, C. E. Priebe, G. Li, and V. Lyzinski. "Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability" arXiv, 2019. URL: http://arxiv.org/abs/1401.3813

  • [3]

    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. DOI: https://doi.org/10.1101/639674 URL: https://doi.org/10.1101/639674

  • [2]

    J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. "Geometric Dimensionality Reduction for Subsequent Classification" arXiv, 2018. URL: https://arxiv.org/abs/1709.01233

  • [1]

    S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. "Signal Subgraph Estimation Via Vertex Screening" arXiv, 2018. URL: https://arxiv.org/abs/1801.07683

Conference Papers

Book Chapters

Technical Reports

  • [23]

    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. DOI: https://doi.org/10.1038/s41592-021-01218-z URL: https://doi.org/10.1038/s41592-021-01218-z

  • [22]

    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. URL: https://arxiv.org/abs/2011.06557

  • [21]

    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. URL: https://osf.io/ek4n3

  • [20]

    Joshua T. Vogelstein. "P-Values in a Post-Truth World" arXiv, 2020. URL: https://arxiv.org/abs/2007.03611

  • [19]

    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. URL: https://arxiv.org/abs/2005.10700

  • [18]

    C. E. Priebe, J. T. Vogelstein, F. Engert, and C. M. White. "Modern Machine Learning: Partition & Vote" bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.04.29.068460 URL: https://www.biorxiv.org/content/early/2020/05/17/2020.04.29.068460

  • [17]

    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. URL: https://arxiv.org/abs/2004.12926

  • [16]

    Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. "Statistical Analysis of Data Repeatability Measures" arXiv, 2020. URL: https://arxiv.org/abs/2005.11911

  • [15]

    D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. "Graphyti: A Semi-External Memory Graph Library for FlashGraph" arXiv, 2019. URL: https://arxiv.org/abs/1907.03335

  • [14]

    H. Helm, J. V. Vogelstein, and C. E. Priebe. "Vertex Classification on Weighted Networks" arXiv, 2019. URL: https://arxiv.org/abs/1906.02881

  • [13]

    D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. "clusterNOR: A NUMA-Optimized Clustering Framework" arxiv, 2019. URL: https://arxiv.org/abs/1902.09527

  • [12]

    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. DOI: https://doi.org/10.1101/479055 URL: https://doi.org/10.1101/479055

  • [11]

    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. DOI: https://doi.org/10.1101/188706 URL: https://doi.org/10.1101/188706

  • [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. URL: http://arxiv.org/abs/1803.03367

  • [9]

    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. URL: https://doi.org/10.1101/188706

  • [8]

    R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. "Robust Estimation from Multiple Graphs under Gross Error Contamination" arXiv, 2017. URL: https://arxiv.org/abs/1707.03487

  • [7]

    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. URL: http://arxiv.org/abs/1705.03297

  • [6]

    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. URL: http://arxiv.org/abs/1604.06414

  • [5]

    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. URL: http://arxiv.org/abs/1602.01421

  • [4]

    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. URL: https://arxiv.org/abs/1604.06414

  • [3]

    A. Sinha, W. Roncal, and N. Kasthuri. "Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes" arXiv, 2014. URL: http://arxiv.org/abs/1404.4800

  • [2]

    M. Kazhdan, R. Burns, B. Kasthuri, J. Lichtman, J. Vogelstein, and J. Vogelstein. "Gradient-Domain Processing for Large EM Image Stacks" arXiv, 2013. URL: http://arxiv.org/abs/1310.0041

  • [1]

    A. Banerjee, J. Vogelstein, and D. Dunson. "Parallel inversion of huge covariance matrices" arXiv, 2013. URL: http://arxiv.org/abs/1312.1869

Other Publications

Funding

External Research Support: Current

  • 2020-2022 NSF, AI Institute: Planning: BI4ALL: Understanding Biological NSF 20-503

    PI: K. Kording
    Role on Project: Co-Investigator
    Term: 01-Oct-2020 to 31-Jul-2022
    Funding to lab, entire period: N/A
    Funding to lab, current year: $79,629 (direct)

    The goal of this project is to plan an AI institution via several meetings and workshops

  • 2020-2025 NSF, Collaborative Research: Transferable, Hierarchical, Expensive, Optimal, Robust, Interpretable Networks NSF 20-540

    PI: R Vidal
    Role on Project: Co-Investigator
    Term: 01-Sep-2020 to 31-Aug-2025
    Funding to lab, entire period: $1,650,000 (direct)
    Funding to lab, current year: $660,000 (direct)

    The goal of this project is to develop a mathematical, statistical and computational frame- work that helps explain the success of current network arcitectures, understand its pitfalls, and guide the design of novel architectures with guaranteed confidence, robustness, inter- pretability, optimality, and transferability

  • 2020 -- Microsoft, Federated Causal Inference for Multi-site Real-World Evidence \& Clinical Trial Analysis Studies in Pandemic Preparedness

    PI: M. Powell
    Role on Project: Co-Investigator
    Term: 01-Aug-2020 to current
    Funding to lab, entire period: N/A
    Funding to lab, current year: N/A

    This project will conduct federated retrospective analyses designed to assess the benefit of off-label drug use by pooling multiple disparate databases, to help prioritize and guide subsequent initiation and recruitment of randomized clinical trials. This will include evaluating the impact of the target drugs on patient outcomes from diseases similar to COVID-19, such as pneumonia or acute respiratory distress, generating artificial datasets using generative adversarial networks to asses performance of methods when 'ground truth' is known, applying the best methods to analyze the effect of the target drugs on the outcomes of COVID-19 patients across hospital systems, and using the results to evaluate the potential of these drugs and suggest guidelines for clinical trials.

  • 2020-2023 NIH, Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes NIH MH-19-147

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Jul-2020 to 30-Jun-2023
    Funding to lab, entire period: $861,240 (direct) $1,416,279 (total)
    Funding to lab, current year: $283,301 (direct) $471,082 (total)

    The goal of this project is to establish a state-of-the-art toolbox for analysis of connectomes, spanning taxa, scale, and complexity. we will develop and extend implementations to enable neurobiologists to (1) estimate latent structure from attributed connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships be- tween connectomes and multivariate phenotypes

  • 2020-2025 NSF, NeuroNex2: Enabling Identification and Impact of Synaptic Weight in Functional Networks NSF 2014862

    PI: K Harris
    Role on Project: Co-Investigator
    Term: 01-Apr-2020 to 31-Mar-2025
    Funding to lab, entire period: $609,294 (direct) $997,719 (total)
    Funding to lab, current year: $121,587 (direct) $199,543 (total)

    The goal is to develop the requisite technology to understand the impact of synaptic weight on functional networks

  • 2020-2025 NSF, CAREER: Foundational Statistical Theory and Methods for Analysis of Populations of Attributed Connectomes NSF 17-537

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Jan-2020 to 31-Dec-2025
    Funding to lab, entire period: $630,230 (total) $384,873 (direct)
    Funding to lab, current year: $126,046 (total) $76,975 (direct)

    The goal is to establish foundaitonal theory and methods for analyzing populations of attributed connectomes

  • 2019-2023 NIH, Brain Networks in Mouse Models of Aging NIH RO1AG066184-01

    PI: A. Badea
    Role on Project: Co-Investigator
    Term: 01-Dec-2019 to 30-Nov-2023
    Funding to lab, entire period: N/A
    Funding to lab, current year: $205,998

    The goal of this grant is to generate connectomes and RNA-seq transcriptomes to characterize and differentiate APOE mice as a model of aging

  • 2019-2022 NIH, Accessible technologies for high-throughput, whole-brain reconstructions of molecularly characterized mammalian neurons NIH RFA-MH-19-148

    PI: M. Muller
    Role on Project: Co-Investigator
    Term: 01-Sep-2019 to 31-Aug-2022
    Funding to lab, entire period: $1,180,445 (total) $753,974 (direct)
    Funding to lab, current year: $383,482 (total) $251,325 (direct)

    The overall goal of the proposal is to develop technologies for the brain wide reconstruction of axonal arbors of molecularly defined neurons. The proposal aims at overcoming barriers in neuronal labeling, imaging and computation to achieve this goal, and to develop a technology platform that can be scaled to all neurons of the brain

  • 2019 -- Microsoft, Microsoft Research Award

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: Unrestricted Gift
    Funding to lab, entire period: $50,000 (total)
    Funding to lab, current year: N/A

    Research and development of neuroscience and connectomes around neuronal circuit and system modeling, application of time-series-of-graphs and dynamics to neuronal signaling analysis and connectomes, and in the abstractions of matter, math, machines that point toward complex systems composed of low-level components

  • 2017 -- 2021 DARPA, Continual Learning Across Synapses, Circuits, and Brain Areas FA8650-18-2-7834

    PI: A. Tolias
    Role on Project: Co-Investigator
    Term: 01-Nov-2017 to 30-Oct-2021
    Funding to lab, entire period: $796,715 (total) $486,666 (direct)
    Funding to lab, current year: $199,179 (total) $121,667 (direct)

    Develop the pre-processing analysis pipeline for the imaging data collected in this project

  • 2017 -- 2021 DARPA, Lifelong Learning Forests FA8650-18-2-7834

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Nov-2017 to 31-Oct-2021
    Funding to lab, entire period: $1,839,308 (total) $1,123,474 (direct)
    Funding to lab, current year: $199,179 (total) $121,667 (direct)

    Lifelong Learning Forests (L2Fs) will learn continuously, selectively adapting to new environ- ments and circumstances utilizing top-down feedback to impact low-level processing, with provable statistical guarantees, while maintaining computational tractability at scale

  • 2017 -- 2022 NIH, Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain NIH 1U19NS104653-01

    PI: F. Engert
    Role on Project: Co-Investigator
    Term: 01-Sep-2017 to 31-Aug-2022
    Funding to lab, entire period: $1,050,000 (total) $655,206 (direct) (JHU sub-award)
    Funding to lab, current year: $210,000 (total) $131,041 (direct)

    Generate a realistic multiscale circuit model of the larval zebrafish’s brain – the multiscale virtual fish (MSVF). The model will span spatial ranges from the nanoscale at the synaptic level, to local microcircuits to inter-area connectivity - and its ultimate purpose is to explain and simulate the quantitative and qualitative nature of behavioral output across various timescales

External Research Support: Completed

  • 2019-2020 NIH, Reproducible imaging-based brain growth charts for psychiatry NIH R01MH120482-01

    PI: T. Satterthwaite
    Role on Project: Co-Investigator
    Term: 01-Aug-2019 to 31-May-2020
    Funding to lab, entire period: \$362,861 (total) \$231,276 (direct)
    Funding to lab, current year: N/A

    Aggregate, harmonize, and analyze existing large-scale pediatric neuroimaging datasets to identify normative and clinical brain growth curves

  • 2018 -- 2021 NSF, SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communication Yeast Cells NSF 1807369

    PI: E. Schulman
    Role on Project: Co-Investigator
    Term: 16-Jul-2018 to 30-Jun-2021
    Funding to lab, entire period: \$263,942 (total) \$172,971 (direct)
    Funding to lab, current year: \$87,980 (total) \$57,657 (direct)

    Provide neuroscience and machine learning expertise to guide the design of the computa- tional learning capabilities of the system

  • 2018 -- 2020 Schmidt Science Foundation, Connectome Coding at the Synaptic Scale Nascent Innovation Grant 128503

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Jan-2018 to 31-Dec-2020
    Funding to lab, entire period: \$250,000 (total)
    Funding to lab, current year: N/A

    Study learning and plasticity at an unprecedented scale, revealing the dynamics of large populations of synapses comprising an entire local cortical circuit. No previously conducted experiment could answer the questions about the dynamics of large populations of synapses, which is crucial to understanding the learning process

  • 2017 -- 2020 NSF, NeuroNex Innovation Award: Towards Automatic Analysis of Multi-Terabyte Cleared Brains NSF 1707298

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Sep-2017 to 31-Aug-2020 (No Cost Extension)
    Funding to lab, entire period: \$959,999 (total) \$588,758 (direct)
    Funding to lab, current year: \$320,000 (total) \$196,252 (direct)

    We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

  • 2017 -- 2020 NIH, CRCNS US-German Res Prop: functional computational anatomy of the auditory cortex NIH 1R01DC016784-01

    PI: J. MRatnanather
    Role on Project: Co-Investigator
    Term: 01-Jul-2017 to 30-Jun-2020
    Funding to lab, entire period: \$747,143 (total) \$458,519 (direct)
    Funding to lab, current year: N/A

    Create a robust computational framework for analyzing the cortical ribbon in a specific region: the auditory cortex

  • 2017 -- 2020 NSF, Multiscale Generalized Correlation: A Unified Distance-Based Correlation Measure for Dependence Discovery NSF 1921310

    PI: S. Cencheng
    Role on Project: Co-Investigator
    Term: 01-May-2017 to 30-Apr-2020
    Funding to lab, entire period: \$200,000 (total) \$124,189 (direct)
    Funding to lab, current year: N/A

    Establish a unified methodology framework for statistical testing in high-dimensional, noisy, big data, through theoretical advancements, comprehensive simulations, and real data experiments

  • 2017 -- 2019 NSF, NeuroNex Technology Hub: Towards the International Brain Station for Accelerating and Democratizing Neuroscience Data Analysis and Modeling NSF 16-569

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 2017 to 2019
    Funding to lab, entire period: \$246,773
    Funding to lab, current year: N/A

    We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

  • 2017 -- 2018 The Kavli Foundation, The International Brain Station 90071826

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 2017 to 2018
    Funding to lab, entire period: \$50,000 (total) \$50,000 (direct)
    Funding to lab, current year: N/A

    Take the first few steps towards building the international brain station

  • 2017 -- 2018 NSF, Brain Comp Infra: EAGER: BrainLab CI: Collaborative, Community Experiments ACI-1649880

    PI: B. Miller
    Role on Project: Co-Investigator
    Term: 2017 to 2018
    Funding to lab, entire period: \$294,599 (total) \$180,736 (direct)
    Funding to lab, current year: N/A

    The BrainLab CI prototype system will deploy an experimental-management infrastruc- ture that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data

  • 2017 -- 2018 DARPA, The Brain Ark 90076467

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 2017 to 2018
    Funding to lab, entire period: \$92,376 (total) \$56,499.08 (direct)
    Funding to lab, current year: N/A

    Characterize the statistical properties of the individual graphs, to identify circuit motifs, both that specialize in a species specific fashion, and that are preserved across species. As a test, will compare the connectomes of sea lions and coyotes

  • 2016 -- 2020 DARPA, D3M: What Would Tukey Do? FA8750-17-2-0112

    PI: C. Priebe
    Role on Project: Co-Investigator
    Term: 01-Oct-2016 to 30-Sep-2020
    Funding to lab, entire period: \$4,406,360 (total) \$2,746,050 (direct)
    Funding to lab, current year: N/A

    Develop theory and methods for generating a discoverable archive of data modeling primi- tives and for automatically selecting model primitives and for composing selected primitives into complex modeling pipelines based on user-specified data and outcome(s) of interest

  • 2016 -- 2019 NSF, A Scientific Planning Workshop for Coordinating Brain Research Around the Globe NIH RFA-MH-19-148

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 2016 to 2019
    Funding to lab, entire period: \$97,950 (total) \$97,950 (direct)
    Funding to lab, current year:

    This travel grant is for the expressed purposes of gathering researchers from around the globe to discuss the new way to further brain research during part one of a two day conference

  • 2016 -- 2019 NSF, A Scientific Planning Workshop for Coordinating Brain Research Around the Globe NSF 1637376

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 2016 to 2019
    Funding to lab, entire period: \$16,327 (total) \$14,491 (direct)
    Funding to lab, current year: N/A

    This travel grant is for the expressed purposes of gathering researchers from around the globe to further discuss advancements in brain research during the second part of a two day conference

  • 2015 -- 2018 DARPA, From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from N66001-15-C-40401

    PI: J. Vogelstein
    Role on Project: Principal Investigator
    Term: 01-Sep-2019 to 31-Aug-2022
    Funding to lab, entire period: \$2,103,091.60 (total) \$1,298,204 (direct)
    Funding to lab, current year: N/A

    Multiple, large, multifarious brain imaging datasets are rapidly becoming standards in neuroscience. Yet, we lack the tools to analyze individual datasets, much less populations thereof. Therefore, we will develop theory and methods to analyze and otherwise make such data available

  • 2014 -- 2016 DARPA, Scalable Grain Graph Analyses Using Big-Memory, High-IPS Compute Architectures N66001-14-1-4028

    PI: R. Burns
    Role on Project: Co-Investigator
    Term: 2014 to 2016
    Funding to lab, entire period: \$39,882 (total) \$28,272 (direct)
    Funding to lab, current year: N/A

    Build software infrastructure to enable analytics on billion node, terabyte sized networks using commodity hardware

  • 2014 -- 2019 NIH, Synaptomes of Mouse and Man NIH R01NS092474

    PI: S. Smith
    Role on Project: Co-Investigator
    Term: 2014 to 2019
    Funding to lab, entire period: \$756,417 (total) \$491,341 (direct)
    Funding to lab, current year: N/A

    The major goals of this project are to discover the synaptic diversity and complexity in mammalian brains, specifically comparing and contrasting humans with mice, the leading experimental animal

  • 2012 -- 2015 National Institute of Biomedical Imaging and Bioengineering, CRCNS: Data Sharing: The EM open Connectome Project RO1EB16411

    PI: R. Burns
    Role on Project: Co-Investigator
    Term: 2012 to 2015
    Funding to lab, entire period: \$70,823 (total) \$46,517 (direct)
    Funding to lab, current year: N/A

    Develop cyberinfrastructure to support management, visualization, storage, and analysis of large-scale electron microscopy data

Invited Talks

Other Talks

Abstracts/Poster Presentations

  • [74]

    L. A. De Silva and J. T. Vogelstein. "Kernel density networks" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022.

  • [73]

    J. Dey, W. LeVine, L. A. De Silva, A. Geisa, and J. T. Vogelstein. "Out-of-distribution Detection Using Kernel Density Polytopes" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022. URL: https://figshare.com/articles/poster/jayanta-NAISys2022_pdf/20070512

  • [72]

    J. J. How, G. Schuhknecht, M. B. Ahrens, F. Engert, and J. T. Vogelstein. "Transfer learning in larval zebrafish (Danio rerio)" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022. URL: https://figshare.com/articles/poster/javier-NAISys2022_pdf/20070509

  • [71]

    B. D. Pedigo, M. Powell, E. W. Bridgeford, M. Winding, C. E. Priebe, and J. T. Vogelstein. "Generative network modeling reveals a first quantitative definition of bilateral symmetry exhibited by a whole insect brain connectome" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022. URL: https://figshare.com/articles/poster/Generative_network_modeling_reveals_a_quantitative_definition_of_bilateral_symmetry_exhibited_by_a_whole_insect_brain_connectome/19610013

  • [70]

    J. M. Shin, L. Isik, and J. T. Vogelstein. "Measure of human-likelihood in tree-based ensemble model and artificial neural networks" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022. URL: https://figshare.com/articles/poster/2022_NAISys_conference_poster_presentation/20070515

  • [69]

    H. Xu and J. T. Vogelstein. "Simplest streaming trees" In From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, Mar. 2022.

  • [68]

    T. L. Athey, J. Sulam, J. Vogelstein, D. Tward, and M. Miller. "Automated Neuron Tracing of Sparse Fluorescently Labeled Neurons" In Neuromatch 3, Nov. 2020.

  • [67]

    E. W. Bridgeford, M. Powell, A. Alyakin, B. Caffo, and J. T. Vogelstein. "Batch Effects are Causal Effects: Applications in Human Functional Connectomes" In Neuromatch 3, Nov. 2020.

  • [66]

    J. Chung, J. Dey, G. Kiar, C. E. Priebe, and J. T. Vogelstein. "Human Structural Connectomes are Heritable" In Neuromatch 3, Nov. 2020.

  • [65]

    V. Gopalakrishnan, J. Chung, E. Bridgeford, J. Arroyo, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Statistical Methods for Multiscale Comparative Connectomics" In Neuromatch 3, Nov. 2020.

  • [64]

    T. Liu, B. D. Pedigo, T. L. Athey, and J. T. Vogelstin. "Hierarchical stochastic block modeling in the Drosophila connectome" In Neuromatch 3, Nov. 2020.

  • [63]

    B. D. Pedigo, M. Winding, T. Orujlu, M. Zlatic, Cardona,Albert, C. E. Priebe, and J. T. Vogelstein. "A quantitative comparison of a complete connectome to artificial intelligence architectures" In NAIsys, Cold Spring Harbor, NY, USA, Nov. 2020.

  • [62]

    B. D. Pedigo, M. Winding, A. Saad-Eldin, T. Liu, A. Cardona, M. Zlatic, C. E. Priebe, and J. T. Vogelstein. "Statistical tools for nanoscale connectomics: clustering neurons in Drosophila larva brain and other applications" In Neuromatch 3, Nov. 2020.

  • [61]

    R. Perry, J. Zorn, S. Czajko, D. S. Margulies, and J. T. Vogelstein. "Permutation-corrected independence testing for high-dimensional fMRI data" In Neuromatch 3, Nov. 2020.

  • [60]

    A. Saad-Eldin, B. D. Pedigo, Y. Park, C. E. Priebe, and J. T. Vogelstein. "NeuroGraphMatch" In Neuromatch 3, Nov. 2020.

  • [59]

    J. T. Vogelstein, H. Helm, B. D. Pedigo, R. Mehta, C. E. Priebe, and C. White. "A Biological Implementation of Lifelong Learning in the Pursuit of Artificial General Intelligence" In NAIsys, Cold Spring Harbor, NY, USA, Nov. 2020.

  • [58]

    J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Impact of Concatenating fMRI Data on reliability for Functional Connectomics" In OHBM and Resting State, Fairmont, Dallas, TX, USA, Sep. 2020.

  • [57]

    J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences" In OHBM and Resting State, Fairmont, Dallas, TX, USA, Sep. 2020.

  • [56]

    J. Hecheng, J. S. Ramirez, J. T. Vogelstein, M. P. Milham, and T. Xu. "Assessing functional connectivity beyond Pearson's correlation" In Fairmont, Dallas, TX, USA, Sep. 2020.

  • [55]

    X. Li, J. Cho, M. P. Milham, and T. Xu. "Improving brain-behavior prediction using individual-specific components from connectivity-based shared response model" In Resting State, Fairmont, Dallas, TX, USA, Sep. 2020.

  • [54]

    E. Bridgeford and J. T. Vogelstein. "Optimal Experimental Design for Big Data: Applications in Brain Imaging" In OHBM, Jun. 2020.

  • [53]

    J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Impact of Concatenating fMRI Data on reliability for Functional Connectomics" In OHBM and Resting State, Fairmont, Dallas, TX, USA, Jun. 2020.

  • [52]

    J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences" In OHBM and Resting State, Fairmont, Dallas, TX, USA, Jun. 2020.

  • [51]

    R. Perry and J. T. Vogelstein. "Identifying Differences Between Expert and Novice Meditator Brain Scans via Multiview Embedding" In OHBM, Jun. 2020.

  • [50]

    B. Falk and J. T. Vogelstein. "NeuroData's Open Data Cloud Ecosystem" In Harvard University, Cambridge, MA, USA, Jul. 2019. URL: https://neurodata.io/talks/25_NeuroDatas_Open_Data_Ecosystem.pdf

  • [49]

    J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Clustering Multi-Modal Connectomes" In OHBM, Rome Italy, Jun. 2019. URL: https://figshare.com/articles/Clustering_Multi-Modal_Connectomes/8309672

  • [48]

    J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Human Structural Connectomes are Heritable" In OHBM, Rome Italy, Jun. 2019. URL: https://figshare.com/articles/Structural_Connectomes_are_Heritable/7800587

  • [47]

    J. Browne, D. Mhembere, T. M. Tomita, J. T. Vogelstein, and R. Burns. "Forest Packing: Fast Parallel Decision Forests" In SIAM International Conference on Data Mining, Calgary, Alberta, Canada, May 2019. URL: https://figshare.com/articles/Forest_Packing_Fast_Parallel_Decision_Forests/8194142

  • [46]

    T. L. Athey and J. T. Vogelstein. "Low-level Neuron Segmentation in Sub-Micron Resolution Images of the Complete Mouse Brain" In Brain Initiative Investigators Meeting, Washington DC, USA, Apr. 2019.

  • [45]

    T. L. Athey and J. T. Vogelstein. "Investigating Neuron Trajectories with Splines" In Brain Initiative Investigators Meeting, Washington DC, USA, Apr. 2019.

  • [44]

    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" In Max Planck /HHMI Connectomics Meeting Berlin, Germany, Apr. 2019. URL: https://figshare.com/articles/GraSPy_an_Open_Source_Python_Package_for_Statistical_Connectomics/7982888

  • [43]

    A. Baden, E. Perlman, F. Collman, S. Smith, J. T. Vogelstein, and R. Burns. "Processing and Analyzing Terascale Conjugate Array Tomography Data" In Berlin, Germany, 2017. URL: https://neurodata.io/talks/berlin_2017.pdf

  • [42]

    E. Perlman. "NEURODATA: ENABLING BIG DATA NEUROSCIENCE" In Kavli, Baltimore, MD, USA, 2017. URL: https://neurodata.io/talks/perlman_kndi_2017.pdf

  • [41]

    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" In Society for Neuroscience, Chicago, IL, USA, Oct. 2015. URL: https://figshare.com/articles/Open_Synaptome_Project/1585165

  • [40]

    J. T. Vogelstein. "Open Connectome Project & NeuroData: Enabling Data-Driven Neuroscience at Scale" In Society for Neuroscience, Chicago, IL, USA, Oct. 2015. URL: https://figshare.com/articles/NeuroData_amp_The_Open_Connectome_Project_Enabling_Big_Data_Neuroscience_at_Scale/1585167

  • [39]

    S. Chen, J. T. Vogelstein, S. Lee, M. Lindquist, and B. Caffo. "High Dimensional State Space Model with L-1 and L-2 Penalties" In ENAR 2015, Miami, FL, USA, Mar. 2015. URL: http://www.enar.org/abstracts/2015_Program_Abstracts_03-02-15.pdf

  • [38]

    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" In Figshare, 2015. URL: https://figshare.com/articles/A_Sparse_High_Dimensional_State_Space_Model_with_an_Application_to_Neuroimaging_Data/1515020

  • [37]

    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" In Figshare, 2015. URL: https://figshare.com/articles/X_Brain_Quantifying_Mesoscale_Neuroanatomy_Using_X_Ray_Microtomography/1585163

  • [36]

    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" In Figshare, 2015. URL: https://figshare.com/articles/Optimal_Design_for_Discovery_Science_Applications_in_Neuroimaging/1515021

  • [35]

    S. A. C. Sikka. "Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac)" In 5th INCF Congress of Neuroinformatics, Munich, Germany, Aug. 2014. URL: https://www.frontiersin.org/10.3389/conf.fninf.2014.08.00117/event_abstract

  • [34]

    J. T. Vogelstein and C. E. Priebe. "Nonparametric Two-Sample Testing on Graph-Valued Data." In Duke Workshop on Sensing and Analysis of HighDimensional Data, Durham, NC, USA, Jul. 2013.

  • [33]

    W. A. K. Gray Roncal. "Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data" In OHBM, Seattle, WA, USA, Jun. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284151

  • [32]

    D. A. B. Mhembere. "Multivariate Invariants from Massive Brain-Graphs" In OHBM, Seattle, WA, USA, Jun. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284154

  • [31]

    Y. Qin, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. Vogelstein, and C. . Priebe. "Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood" In OHBM, Seattle, WA, USA, Jun. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284153

  • [30]

    D. L. Sussman, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. T. Vogelstein, and C. E. . Priebe. "Massive Diffusion MRI Graph Structure Preserves Spatial Information" In OHBM, Seattle, WA, USA, Jun. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284155

  • [29]

    D. Koutra, Y. Gong, S. Ryman, R. Jung, J. T. Vogelstein, and C. Faloutsos. "Are All Brains Wired Equally?" In Proceedings of the 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Seattle, WA, USA, (4.2)1:3, Jun. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284149

  • [28]

    A. A. V. Raag D. "Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI" In Proc ISMRM, Salt Lake City, UT, USA, Apr. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284146

  • [27]

    N. A. S. Sismanis. "Feature Clustering from a Brain Graph for Voxel-to-Region Classification" In 5th Panhellic Conference on Biomedical Technology, Athens, Greece, Apr. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284143

  • [26]

    E. A. A. M. Pnevmatikakis. "Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium inaging data" In COSYNE, Salt Lake City, UT, USA, Mar. 2013. URL: http://dx.doi.org/10.6084/m9.figshare.1284170

  • [25]

    J. T. Vogelstein and others. "Anomaly Screening and Clustering of Multi-OBject Movies via Multiscale Structure Learning" In DARPA XDATA Colloquium, 2013.

  • [24]

    J. A. S. Vogelstein. "BRAINSTORM towards clinically and scientifically useful neuroimaging analytics" In Neuroinformatics, Munich, Germany, Sep. 2012. URL: http://dx.doi.org/10.6084/m9.figshare.1284173

  • [23]

    J. T. A. B. Vogelstein. "Statistical Connectomics" In Janelia Farm conference, Statistical Inference and Neuroscience, Loudoun County, VA, USA, May 2012. URL: http://dx.doi.org/10.6084/m9.figshare.1284174

  • [22]

    W. R. A. K. Gray. "Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data" In Cold Spring Harbor Laboratory, Neuronal Circuits, Cold Spring Harbor, NY, USA, 2012. URL: http://dx.doi.org/10.6084/m9.figshare.1284176

  • [21]

    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" In Society for Neuroscience, Washington DC, USA, Oct. 2011. URL: http://dx.doi.org/10.6084/m9.figshare.1284177

  • [20]

    J. T. Vogelstein, D. E. Fishkind, D. L. Sussman, and C. E. Priebe. "Large graph classification: theory and statistical connectomics applications" In IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA, Oct. 2011. URL: http://dx.doi.org/10.6084/m9.figshare.1284184

  • [19]

    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" In Society for Neuroscience, Washington DC, USA, Oct. 2011. URL: http://dx.doi.org/10.6084/m9.figshare.1284178

  • [18]

    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" In IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA, Oct. 2011.

  • [17]

    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" In , Sep. 2011. URL: http://dx.doi.org/10.6084/m9.figshare.1284181

  • [16]

    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" In Organization for Human Brain Mapping, Quebec City, Canada, Jun. 2011. URL: http://dx.doi.org/10.6084/m9.figshare.1284179

  • [15]

    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" In DARPA Neural Engineering, Science and Technology Forum, San Diego, CA, USA, Nov. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1285815

  • [14]

    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" In DARPA Neural Engineeering, Science and Technology Forum, San Diego, CA, USA, Nov. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1285813

  • [13]

    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" In Organization for Human Brain Mapping, Barcelona, Spain, Jun. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1285813

  • [12]

    J. T. Vogelstein, R. Vogelstein, and C. E. Priebe. "A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains" In CSHL conference on Neural Circuits, Cold Shore Harbor, NY, USA, Mar. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1284694

  • [11]

    J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Confirming Neural Circuit Inference from Population Calcium Imaging" In COSYNE, Salt Lake City, UT, USA, Feb. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1284693

  • [10]

    J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Inferring Neural Circuit Inference from Population Calcium Imaging" In COSYNE, Salt Lake City, UT, USA, Feb. 2010. URL: http://dx.doi.org/10.6084/m9.figshare.1285819

  • [9]

    J. T. Vogelstein, Y. Mishchchenko, A. M. Packer, T. A. Machado, R. Yuste, and L. Paninski. "Towards Confirming Neural Circuits from Population Calcium Imaging" In NIPS Workshop on Connectivity Inference in Neuroimaging, Whistler, BC, Canada, Dec. 2009. URL: http://dx.doi.org/10.6084/m9.figshare.1285822

  • [8]

    J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Inferring Neural Circuit Inference from Population Calcium Imaging" In COSYNE, Salt Lake City, UT, USA, Feb. 2009. URL: http://dx.doi.org/10.6084/m9.figshare.1285821

  • [7]

    J. T. Vogelstein, B. Babadi, B. Watson, R. Yuste, and L. Paninski. "From Calcium Sensitive Fluorescence Movies to Spike Trains" In Society for Neuroscience, Washington DC, USA, Nov. 2008. URL: http://dx.doi.org/10.6084/m9.figshare.1285824

  • [6]

    J. T. Vogelstein, B. Babadi, and L. Paninski. "Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging" In COSYNE, Salt Lake City, UT, USA, Feb. 2008. URL: http://dx.doi.org/10.6084/m9.figshare.1285826

  • [5]

    J. T. Vogelstein and L. Paninski. "Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging" In Integrative Approaches to Brain Complexity, 2008. URL: http://dx.doi.org/10.6084/m9.figshare.1285827

  • [4]

    J. T. Vogelstein, B. Jedynak, K. Zhang, and L. Paninski. "Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging" In Society for Neuroscience, San Diego, CA, USA, Nov. 2007. URL: http://dx.doi.org/10.6084/m9.figshare.1285846

  • [3]

    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" In COSYNE, Salt Lake City, UT, USA, Feb. 2007. URL: http://dx.doi.org/10.6084/m9.figshare.1285828

  • [2]

    J. T. Vogelstein and K. Zhang. "A novel theory for simultaneous representation of multiple dynamic states in hippocampus" In Society for Neuroscience, San Diego, CA, USA, 2004.

  • [1]

    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" In Society for Neuroscience, Orlando, FL, USA, 2002.

Educational Activities

Teaching Experience - Ongoing Courses

Teaching Experience - One-Time

  • Spring '19 Systems Bioengineering II EN.580.422, Guest Lecturer, JHU, 2 Lectures.
  • Spring '19 Computational Neuroscience AS.080.321, Guest Lecturer, JHU, 2 Lectures.
  • Spring '18 Systems Bioengineering II EN.580.422, Guest Lecturer, JHU, 2 Lectures.
  • Spring '18 Computational Neuroscience AS.080.321, Guest Lecturer, JHU, 2 Lectures.
  • Spring '17 Systems Bioengineering II EN.580.422, Guest Lecturer, JHU, 2 Lectures.
  • Spring '16 Systems Bioengineering II EN.580.422, Guest Lecturer, JHU, 2 Lectures.
  • Winter '16 Introduction to Connectomics EN.600.221, Guest Lecturer, JHU, 1 Lecture.
  • Fall '16 BME Modeling and Design EN.580.111, Guest Lecturer, JHU, 1 Lecture.
  • Fall '15 Introduction to Computational Medicine Course Co-Director, JHU.

Educational Workshops

Mentorship

Research Track Faculty Mentorship

  • 07/19 -- 08/20 Ronak Mehta MSE Research Assistant BME, JHU

    Finalizing three manuscripts on (1) uncertainty forests, (2) time-series dependence quantification, and (3) lifelong learning forests

  • 03/19 -- 05/20 Anton Alyakin BSE Assistant Research Engineer BME, JHU

    Worked on various problems in statistical graph inference

  • 02/19 -- 12/19 Hayden Helm MSE Assistant Research Faculty BME, JHU

    Lead research efforts developing theory and methods for lifelong learning

  • 08/16 -- 08/18 Eric Perlman Ph.D. Assistant Research Faculty BME, JHU

    ead Scientist in developing storage, transfer, and visualization solutions for large data in our cloud infrastructure

  • 03/16 -- 06/20 Jesse Patsolic MA Assistant Research Faculty BME, JHU

    Lead developer converting our extensions to decision forests to be merged into sklearn

Staff Research Scientists

  • 09/20 -- Jong Shin MS Software Engineer BME, JHU

    Currently investigating the effect of inductive bias innately coinciding with various machine learning models

  • 03/20 -- 08/22 Ali Geisa MS Research Assistant BME, JHU

    Researching progressive and lifelong learning theory

  • 06/19 -- 08/20 Devin Crowley BS Research Assistant BME, JHU

    Lead developer of our scalable Python implementation of LDDMM

  • 06/18 -- 12/19 Benjamin Falk Ph.D. Research Engineer BME, JHU

    Lead software engineer, oversees all development projects, solely responsible for all cloud infrastructure

Postdoctoral Fellows

  • 11/20 -- Javier Josue How Ph.D. Postdoctoral Fellow Neurosciences, UCSD

    Javier studies how larval zebrafish learn how to perform a task under one situation, and use this knowledge to learn another task more quickly. He hopes to use this understanding of biological transfer learning to improve machine learning, which tends to be unable to complete this feat.

  • 07/19 -- 08/21 Austin Grave Ph.D. Post-doctoral Fellow Kavli NDI, JHU

    Co-Advised by Prof. Richard Huganir, Department of Neuroscience. Working on understanding whole brain synaptic plasticity using genetic engineering and light microscopy imaging

  • 07/19 -- 08/21 Celine Drieu Ph.D. Post-doctoral Fellow Kavli NDI, JHU

    Co-Advised by Assitant Prof. Kuchibhotla, Department of Psychological and Brain Sciences. Working on understanding learning and memory using two-photon calcium imaging

  • 08/18 -- 08/20 Jesús Arroyo Ph.D. Post-doctoral Fellow CIS, JHU

    Worked on graph matching and joint graph embedding

  • 07/18 -- 07/20 Audrey Branch Ph.D. Post-doctoral Fellow Kavli NDI, JHU

    Co-Advised by Prof Michela Gallagher, extending brain clearing experimental technology from mice to rats. Currently with a manuscript on biorxiv

  • 09/16 -- 08/18 Cencheng Shen Ph.D. Post-doctoral Fellow CIS, JHU

    Developed Multiscale Graph Correlation, which is currently the premiere hypothesis testing framework, and about to be integrated into SciPy, by far the world's leading scientific computing package. Currently an Assistent Professor in Department of Statistics at University of Delaware, and still an actice collaborator and grantee

  • 06/16 -- 07/17 Guilherme Franca Ph.D. Post-doctoral Fellow CIS, JHU

    Worked on non-parametric clustering, with an article about to be accepted in PAMI, the leading machine learning journal. Currently a postdoc for Rene Vidal

  • 05/16 -- 06/17 Leo Duan Ph.D. Post-doctoral Fellow CIS, JHU

    Went on to do a second postdoc with Leo Dunson (who I did my second postdoc with). Currently an Assistant Professor at University of Florida

  • 08/14 -- Tyler Tomita MSE Postdoctoral Fellow BME, JHU

    Developed Sparse Projection Oblique Randomer Forest in his dissertation, currently the best performing machine learning algorithm on a standard suite of over 100 benchmark problems. Currenly a postdoc with Assistant Prof. Chris Honey of Psychology and Brain Sciences

Ph.D. Students

  • 05/22 -- Jeremy Welland Ph.D. PhD Student (Rotation) BME, JHU

  • 02/22 -- Alice Qingyang Wang Bsc PhD candidate Neuroscience, JHU

  • 01/22 -- Noga Mudrik Ph.D. PhD Student (Rotation) BME, JHU

  • 08/21 -- Ashwin De Silva BS PhD Student BME, University of Moratuwa

    Statistical Machine Learning

  • 01/21 -- Haoyin Xu MSE PhD Student BME, JHU

    A Research Assistant who was also a Master's student in the NeuroData lab, maintainer of proglearn, working on streaming trees and forests

  • 08/20 -- Kaleab A. Kinfu MSE PhD Student BME, JHU

    Kaleab studied double descent phenomena in decision forests and deep learning methods and developed 'Partition and Decode' -- a framework that formalizes an implicit internal representation of several modern machine learning methods. He is currently a Ph.D. student in CS at JHU.

  • 05/20 -- Tingshan Liu B.A. PhD Student Math \& Neuro, Smith College

    Implementing and applying clustering algorithms to the connectomes of inset nervous systems.

  • 08/19 -- Eric Bridgeford BSE PhD advisee Department of Biostatistics, JHU

    Dissertation will focus on statistics of human connectomes and mitigating batch effects. Already first author on several manuscripts under review, and spearheads collaboration with Prof Brian Caffo at Biostatistics

  • 08/19 -- Mike Powell MSE PhD Candidate BME, Johns Hopkins University

    Mike has studied drug-repurposing options for potential COVID-19 treatments, proposed methodological improvements and best practices for drug-repurposing studies, and developed a taxonomy for describing and quantifying feature importance in machine learning models.

  • 07/19 -- Jayanta Dey MSE PhD advisee BME, JHU

    Currently working on lifelong learning that aims at training a machine learning model on multiple tasks and transferring knowledge among tasks

  • 06/19 -- Sambit Panda MSE PhD Student BME, JHU

    A Ph.D. student who was also a Master's student in the NeuroData lab. Currently, the maintainer of the `hyppo` package, and works on creating more powerful and efficient multivariate hypothesis tests.

  • 05/19 -- Jaewon Chung MSE PhD Student BME, JHU

    Data science for macroscale connectomes. Co-creator and maintainer of `graspologic`, a Python package for network statistics.

  • 01/19 -- Thomas Athey BS PhD Candidate BME, JHU

    Tommy analyzes terabyte-scale full brain images from light microscopy with a focus on neuromorphology. His expertise is in statistics and computer vision.

  • 08/18 -- Ben Pedigo BS PhD Candidate BME, JHU

    Data science for nanoscale connectomes. Co-creator and maintainer of `graspologic`, a Python package for network statistics.

  • 08/18 -- 06/2022 Meghana Madyastha BSE PhD Co-advisee CS, JHU

    Dissertation will focus on computational aspects of accelerationg learning and inference using decision forests

  • 08/16 -- 12/21 Vikram Chandrashekhar BSE PhD advisee BME, JHU

    Dissertation has focused on extending LDDMM to whole cleared brain datasets, spearheads collaboration with Prof. Karl Deisseroth’s lab at Stanford, one of the world’s leading neuroscientists

Visiting Doctoral Student

  • 03/19 -- 09/19 Derek Pisner MSE PhD advisee JHU/UT, Austin

    Worked on the ndmg pipeline, developing direct streamline normalization for structural connectome generation

M.S. Students

  • 02/22 -- Yuxin Bai MSE MS advisee BME, JHU

  • 05/20 -- 12/21 Ali Saad-Eldin BSE MS advisee BME, JHU

    Working on implementing and improving cominatorial optimization algorithms, specifically the Quadratic Assignment Problem

  • 02/20 -- 12/20 Will LeVine MS advisee BME, JHU

    Exploring different sub-algorithms within progressive learning to alleviate harmful effects that resultfrom training on unhelpful data

  • 01/20 -- 08/22 Shreya Singh BS Graduate Researcher BME, JHU

    'brainlit' package, aws and azure data management

  • 07/19 -- 04/22 Ross Lawrence BSE MS advisee BME, JHU

    Lead m2g developer, maintainer of neuroparc, MRI connectome repositories

  • 06/19 -- 12/20 Bijan Varjavand BSE MS advisee BME, JHU

    Submitted manuscript to PAMI on advancing statistics on populaitons of networks

  • 06/19 -- 05/21 Vivek Gopalakrishnan MSE Combined BS/MSE Student BME, JHU

    Vivek developed multiscale hypothesis tests for multi-subject connectomics datasets, and is currently a PhD student in Medical Engineering and Medical Physics at the Harvard-MIT Program in Health Sciences and Technology.

  • 01/19 -- 06/21 Ronan Perry MSE MSE/BS Student BME, JHU

    Ronan studied random forest methods for structured data and improved prediction calibration, developed nonparametric hypothesis testing tools, and explored novel complexity measures of neural networks. He is currently a Fulbright Germany scholar with Bernhard Scholkopf.

  • 10/18 -- 04/22 Alex Loftus BSE MS advisee BME, JHU

    graph stats book, 'graspologic' package, ndmg development

  • 06/18 -- 06/19 Drishti Mannan BSE MS advisee BME, JHU

    Preparing manuscript introducing novel specification for large attributed networks

  • 08/14 -- 06/17 Greg Kiar BSE MSE advisee BME, JHU

    Developer of m2g, the only existing "soup to nuts" pipeline for both functional and diffusion pipelines, co-first author of manuscript under review. Currently a PhD student at McGill University

Undergraduate Students

  • 06/22 -- Audrey Herskovits BS Undergraduate (Visiting) BME, JHU

  • 06/22 -- Sejal Srivastava BS Undergraduate BME, JHU

  • 09/21 -- Kareef Ullah Undergraduate Researcher BME, JHU

    Assisted with fixing issues in graspologic and hyppo

  • 08/20 -- 05/21 Alisha Kodibagkar Undergraduate Researcher BME, JHU

    Assisting in the integration of brainlit packages with Azure services

  • 05/20 -- 06/2022 Diane Lee Undergraduate Researcher BME, JHU

    Assisting in the maintenance of graspologic

  • 06/21 -- 08/21 Dominique Allen Undergraduate Researcher BME, JHU

    Assisted Thomas Athey in his work with statistics and computer vision

  • 06/19 -- 12/19 Richard Guo Undergraduate Researcher BME, JHU

    Developed uncertainty forests, an approach for estimated posterior class probabilities, conditional entropy, and mutual information for high-dimensional data common in brain science applications

  • 06/15 -- 08/16 Albert Lee BSE Undergraduate BME, JHU

    Developed big data visualization tools

  • 06/15 -- 12/15 Ron Boger BSE Undergraduate Researcher BME, JHU

    Worked at a computational medicine start-up in Silicon Valley, worked on high-dimensional low-sample size theory

  • 05/15 -- 05/16 Jordan Matelsky BSE BME, JHU

    Currently a data scientist at APL, developed a number of simple WebApps in support of big data management

  • 02/15 -- 05/16 Ivan Kuznetsov BSE BME, JHU

    Currently an MD, PhD Candidate at the UPenn, winner of Soros Fellowship, worked on analysis of data from Dr. Daniel Amen, developed matrix exploratory data analysis package.

Highschool Student

  • 05/21 -- 08/21 MyCo Le High School Intern BME, JHU

  • Summer '19 Kiki Zhang Summer Intern BME, JHU

  • Summer '19 Sander Shulhoff Summer Intern BME, JHU

  • Summer '19 Shiyu Sun Summer Intern BME, JHU

    Applied to BME PhD Program in Fall 2020

  • Summer '18 Papa Kobina Van Dyck Summer Intern BME, JHU

    Applied to PhD Program in Fall 2019

Thesis Committee Service (BME unless noted otherwise)

  • 2019 Browne, James Computer Science JHU Ph.D. Student, Graduated 2019
  • 2019 Mhembere, Disa Computer Science JHU Ph.D. Student, Graduated 2019
  • 2018 Kutten, Kwame JHU Ph.D. Student, Graduated 2018
  • 2018 Wang, Shangsi Applied Mathematics and Statistics JHU Ph.D. Student, Graduated 2018
  • 2018 Tang, Runze Applied Mathematics and Statistics JHU Ph.D. Student, Graduated 2018
  • 2018 Lee, Youjin Biostatistics JHU Ph.D. Student, Graduated 2018
  • 2017 Zheng, D Computer Science JHU Ph.D. Student, Graduated 2017
  • 2017 Binkiewicz, Norbert Statistics University of Wisconsin Ph.D. Student, Graduated 2017
  • 2016 Gray-Roncal, Will Computer Science JHU Ph.D. Student, Graduated 2016

Service

Grant Review Service

  • 2015 NSF Review Panel Review for NSF BIG DATA Program

University Service

  • Winter '19 Track Organizer AI in Healthcare: From Bench to Bedside

    Organizer for Breakout Topic Sessions on artificial intelligence

  • 08/15 – 07/18 Co-Developer Computational Medicine Minor, JHU, Baltimore, MD, USA

    http://icm.jhu.edu/academics/undergraduate-minor/

  • 2015 – 2017 Co-Founder and Faculty Advisor MedHacks

    http://medhacks.org/ Medhacks is one of the first and largest hackathons dedicated specifically to hacking on medical advances, started entirely by BME undergrads at JHU

  • 08/14 – 08/18 Director of Undergraduate Studies Institute for Computational Medicine, JHU, Baltimore, MD, USA

    http://icm.jhu.edu/academics/undergraduate-minor/

Department Service

  • 2019 Member Search Committee, BME, Neuroengineering, 2019
  • 2019 Member Search Committee, BME, Data Science, 2019
  • 2018 Member Search Committee, BME, Neuroengineering, 2018

Service in Scientific Community

  • 2019 – Mentor Black in AI
  • 2017 – Scientific Advisory Board NSF NeuroNex

    Enhanced resolution for 3DEM analysis of synapses across brain regions and taxa. Provided scientific, computational, and statistical guidance to a flagship NSF funded BRAIN Initiative program

  • 2017 – Chair of Committee of Data Cores U19 Data Cores

    The U19 program is NIH's flagship BRAIN Initative program, with five original awardees, each with a dedicated Data Core and designated PI. I was elected the chair of the committee of Data Core PIs

  • 2017 Consultant for Nature Publishing Group The journal Nature

    The journal Nature, flagship journal of Nature Publishing Group, decided to create a ``Code and Software Submission Checklist''. They consulted me on their first draft, and I helped re-write it.

  • 2011 – Open Connectome Project

    The co-founder of the ``Open Connectome Project'' (OCP), for several years, I was the only neuroscientist that could easily store, manage, and analyze very big datasets, spanning first tens of terabytes, and then hundreds. For that reason, I was an essential co-author on a number of big data papers. Specifically, though I sometimes contributed relatively little to the scientific ideas, I often was required to complete, visualize, and/or share the data. Perhaps more importantly, both funding agencies and journals began mandating that these large datasets be publicly shared, and OCP was literally the only option. This is despite often not having funding, nor being a co-author, on the manuscripts

  • 2010 – AWS Open Neuro Data Registry

    Our lab co-founded the https://registry.opendata.aws/open-neurodata/ Registry of Open Data on Amazon Web Serivces (AWS). The implication of this is that now, pending a few minor considerations, any neuroscientist that collects large image data can deposit it online for free. This means that neither they nor we must request funding to store the data. Our lab maintains this repository, but only by virtue of ensuring instructions for uploading, visualizing, and downloading are up to date, and acting as a gatekeeper to ensure only appropriate data are deposited there

Journal Service: Editorial Board

  • 2019 – Associate Editor

    Journal of the American Statistical Association

  • 2018 – Editor

    Neurons, Behavior, Data analysis, and Theory

  • 2016 Guest Associate Editor

    PLoS Computational Biology

Journal Service: Conference and Journal Reviewer

  • Annals of Applied Statistics (AOAS)
  • Bioinformatics
  • International Conference on Learning Representations (ICLR)
  • Network Science
  • Current Opinion in Neurobiology
  • Biophysical Journal
  • IEEE International Conference on eScience
  • IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
  • IEEE Global Conference on Signal and Information Processing (GlobalSIP)
  • IEEE Signal Processing Letters
  • IEEE Transactions on Signal Processing
  • Frontiers in Brain Imaging Methods
  • Journal of Machine Learning Research (JMLR)
  • Journal of Neurophysiology
  • Journal of the Royal Statistical Society B (JRSSB)
  • Nature Communications
  • Nature Methods
  • Nature Reviews Neuroscience
  • Neural Computation
  • Neural Information Processing Systems (Neurips)
  • NeuroImage
  • Neuroinformatics
  • PLoS One
  • PLoS Computational Biology

Conferences and Hackathon Organizer

  • Summer '20 Co-Chair SciPy mini-symposium: Biology and Bioinformatics
  • Winter '19 Organizer Decision Forest Hackathon
  • Summer '19 Organizer NeuroData Workshop

    https://neurodata.devpost.com/ Hackashop to train brain scientists in machine learning for big data (50) participants from around the country

  • March '19 Organizer Neuro Reproducibility Hackashop

    https://brainx3.io/ Hackashop to train brain scientists in best practices in reproducible science, co-organized with two startups: Vathes, LLC and Gigantum (50 participants)

  • Spring '18 Organizer NeuroData Hackathon
  • Fall '17 Organizer NeuroData Mini-Hackathon
  • Summer '17 Organizer NeuroStorm

    https://brainx2.io/ Workshop to bring together thought leaders from academia, national labs, industry, and non-profits around the world to take next steps towards accelerating brain science discovery in the cloud (50 participants and 5 observers from funding institutions)

  • 2016 Organizer Global Brain Workshop

    http://brainx.io/ First ever international Brain Initiative workshop, bringing together leaders from around the world, covered by Nature and Science (75 participants)

  • 2016 Co-Organizer Brains and Bits: Neuroscience Meets Machine Learning, NIPS Workshop

    http://www.stat.ucla.edu/~akfletcher/brainsbits_overview.html

  • Winter '15 Organizer Hack@NeuroData

    http://hack.neurodata.io/

  • 2015 Co-Organizer BigNeuro2015: Making Sense of Big Neural Data, NIPS Workshop

    http://neurodata.io/bigneuro2015

  • 2012 Co-Organizer Scaling up EM Connectomics Conference

    https://openwiki.janelia.org/wiki/download/attachments/8687459/final+agenda+EM+Connectomics+100512.pdf The world's first connectomics workshop, now run annually alternating between Janelia Research and Max Plank locations (80 participants)

Awards and Recognition

Individual

  • 2002 Dean’s List Washington University

Shared

  • 2019 Kavli NDI Distinguished Postdoctoral Fellow Celine Drieu, PhD
  • 2019 Kavli NDI Distinguished Postdoctoral Fellow Austin Graves, PhD
  • 2019 Kavli NDI Distinguished Postdoctoral Fellow Audrey Branch, PhD
  • 2019 Winner of Pistritto Fellowship. Vivek Gopalakrishnan
  • 2017 Best Presentation Award HPDC Mhembere et al.
  • 2017 Nonparametric Statistics of the American Statistical Association Student Paper Award Lee et al.
  • 2014 F1000 Prime Recommended Vogelstein et al.
  • 2013 Spotlight Neural Information Processing Systems (NIPS)
  • 2011 Trainee Abstract Award Organization for Human Brain Mapping
  • 2008 Spotlight Computational and Systems Neuroscience (CoSyNe)

Other Media

Professional/Social Media Presence

  • @neuro_data Twitter account with a approximately 7,000 followers, over 250K impressions in December 2019, and approximately 100 new followers, and upwards of 100 new tweets, per month, and 25 link clicks per day. Follower demographics include <50% high school graduates, 46% female
  • Bits and Brains Professional blog reguarding all things academic, neurological, and statistical, with approximately 30 blog posts, approximately one new post per month (9,000 page views, 3,200 unique users)
    Most Popular Post: 10 Simple Rules to Write a Paper from Start to Finish
  • medium.com/@progl My Medium account where I post articles on both personal and professional topics

Translation / Technology Transfer Activities

Open Datasets

  • 2019 – Templier et al. (2019) URL: https://neurodata.io/data/templier2019

    The non-destructive collection of ultrathin sections onto silicon wafers for post-embedding staining and volumetric correlative light and electron microscopy using MagC. MagC allows the correlative visualization of neuroanatomical tracers within their ultrastructural volumetric electron microscopy context

    0 citations, 119 unique visitors

  • 2018 – Bloss et al. (2018) URL: https://neurodata.io/data/bloss2018

    Images of CA1 pyramidal neurons for analysis involving feature-selective firing as a result of dendritic integration of inputs from multiple brain regions. Show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons

    20 citations, 530 unique visitors

  • 2018 – Branch (2018) URL: https://neurodata.io/data/branch18

    Adult generated neurons in aging M. musculus imaged using array tomography, multi-spectral light microscopy, and electron microscopy

    2 citations, 223 unique visitors

  • 2017 – Allen Atlas URL: https://neurodata.io/data/allen_atlas

    Anatomical reference atlases that illustrate the adult mouse brain in coronal and sagittal planes. They are the spatial framework for datasets such as in situ hybridization, cell projection maps, and in vitro cell characterization. http://atlas.brain-map.org/

    142 citations, 1058 unique visitors

  • 2017 – Hildebrand et al. (2017) URL: https://neurodata.io/data/hildebrand17

    A multi-resolution serial-section electron microscopy data set containing the anterior quarter of a 5.5 days post fertilization larval zebrafish, including its complete brain acquired by Hildebrand and colleagues. Electron micrographs and reconstructions are available for view in CATMAID

    70 citations, 1,014 unique visitors

  • 2017 – Tobin et al. (2017) URL: https://neurodata.io/data/tobin17

    Wiring variations that enable and constrain neural computation in a sensory microcircuit

    28 citations, 43 unique visitors

  • 2016 – Bloss et al. (2016) URL: https://neurodata.io/data/bloss2016

    Images of molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites collected using correlative light-electron microscopy and large-volume array tomography

    41 citations, 701 unique visitors

  • 2016 – Dyer et al. (2016) URL: https://neurodata.io/data/xbrain

    Mesoscale (1 cubic micron resolution) resolution images generated with the use of synchrotron X-ray microtomography (microCT) from millimeter-scale volumes of mouse brain. X-ray tomography promises rapid quantification of large brain volumes

    21 citations, 216 unique visitors

  • 2016 – Lee et al. (2016) URL: https://neurodata.io/data/lee16

    Electron microscopy data collected at $4 \times 4 \times 40$ nm per voxel from the visual cortex in Mouse V1 used in a study of an excitatory network

    132 citations, 725 unique visitors

  • 2016 – Wanner et al. (2016 URL: https://neurodata.io/data/wanner16

    Serial block face scanning EM (SBEM) and conductive sample embedding image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of $9.25 \times 9.25 \times 25$ nm3

    12 citations, 328 unique visitors

  • 2015 – Amunts et al. (2015) URL: https://neurodata.io/data/bigbrain

    BigBrain is an ultrahigh-resolution three-dimensional model of a full human brain at 20 micrometer resolution, enabling an unprecedented look into the human brain at micro- and macro-scopic scale

    262 citations, 1,041 unique visitors

  • 2015 – Bhatla et al. (2015) URL: https://neurodata.io/data/bhatla15

    Nikhil Bhatla and Rita Droste in Bob Horvitz's Lab reconstruction of the anterior half of the C. elegans feeding organ, the pharynx. Volumes for three adult hermaphrodite worms include volumetric tracing of all neurons, selected cell types, I2 neuron synapses. 50 nm thick sections with an image resolution of 2 nm per pixel

    16 citations, 467 unique visitors

  • 2015 – Collman et al. (2015) URL: https://neurodata.io/data/collman15

    Mouse cortex collected using conjugate array tomography (AT), a volumetric imaging method that integrates immunofluorescence and EM imaging modalities in voxel-conjugate fashion

    69 citations, 382 unique visitors

  • 2015 – Deisseroth et al. (2015) URL: https://neurodata.io/data/tomer15

    Twelve CLARITY mouse brains (5 wild type controls and 7 behaviorally challenged) were prepared by Li Ye, and imaged using CLARITY-Optimized Light-sheet Microscopy (COLM) (whole brain COLM imaging and data stitching performed by R. Tomer, in preparation)

    5 citations, 208 unique visitors

  • 2015 – Harris et al. (2015) URL: https://neurodata.io/data/kharris15

    Three volumes of hippocampal CA1 neuropil in adult rat imaged by the laboratory of Kristen M Harris, PhD, at an XY resolution of ~2 nm on serial sections of ~50-60 nm thickness

    9 citations, 463 unique visitors

  • 2015 – Kasthuri et al. (2015) URL: https://neurodata.io/data/kasthuri15

    Saturated reconstruction of a sub-volume of mouse neocortex collected using automated technologies in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components are rendered and itemized in a database. Provides access to the complexity of the neocortex and enables further data-driven inquiries

    323 citations, 1,299 unique visitors

  • 2015 – Micheva et al. (2015) URL: https://neurodata.io/data/kristina15

    Multi-channel array tomography data of the barrel cortex of an adult mouse (C57BL/6J)

    57 citations, 190 unique visitors

  • 2015 – Ohyama et al. (2015) URL: https://neurodata.io/data/acardona_0111_8

    The side view of the approximately 7,000 neurons reconstructed so far, either in full or partially, of the approximately 12,000 neurons of the central nervous system of Drosophila larva. The 0111-8 data set was originally sectioned and imaged by Richard D. Fetter and his two tech assistants

    136 citations, 299 unique visitors

  • 2015 – Randlett et al. (2015) URL: https://neurodata.io/data/zbrain_atlas

    Zebrafish brain atlas with surface mesh of different regions intended for the analysis of whole-brain activity mapping

    124 citations, 498 unique visitors

  • 2014 – Weiler (2014) URL: https://neurodata.io/data/weiler14

    Images of whisker-associated barrel columns of mouse somatosensory cortex stained with antibodies against selected antigens (DAPI, YFP), and indirect immunofluorescence. Images collected by the lab of Stephen J Smith

    6 citations, 123 unique visitors

  • 2013 – Bumbarger et al. (2013) URL: https://neurodata.io/data/bumbarger13

    Serial, thin section data generated by Dan Bumbarger in Ralf Sommer's lab in order to compare the pharyngeal connectomes of the pharyngeal nervous system between Caenorhabditis elegans and Pristionchus pacificus. In P. pacificus they found clearly homologous neurons for all of the 20 pharyngeal neurons in C. elegans, and massive rewiring of synaptic connectivity between the two species

    67 citations, 22 unique visitors

  • 2013 – Takemura et al. (2013) URL: https://neurodata.io/data/takemura13

    The right part of the brain of a wild-type Oregon R female fly that was serially sectioned into 40-nm slices. A total of 1,769 sections, traversing the medulla and downstream neuropils, were imaged at a magnification of 35,000X

    323 citations, 144 unique visitors

  • 2011 – Bock et al. (2011) URL: https://neurodata.io/data/bock11

    Volume of mouse primary visual cortical data, spanning layers 1, 2/3, and upper layer 4 collected as electron microscope (EM) data and two-photon microscopy data collected by Davi Bock, Ph.D. and Wei-Chung Allen Lee, Ph.D.. Images have a resolution of 4x4x45 cubic nanometers

    430 citations, 511 unique visitors

Open-Source Software: Active

  • 2020 – ProgLearn (Progressive Learning) URL: https://github.com/neurodata/ProgLearn

    A Python package for exploring and using progressive learning algorithms

    22 stars, 29 forks, 37 downloads/month

  • 2019 – ARDENT (Affine and Regularized Deformative Numeric Transform) URL: https://github.com/neurodata/ardent

    A Python package for performing automated image registration using LDDMM

    10 stars, 5 forks

  • 2019 – graspologic (Graph Statistics) URL: https://neurodata.io/graspy/

    Co-developed with Microsoft Research: Utilities and algorithms designed for processing and analysis of graphs with specialized graph statistical algorithms

    134 stars, 56 forks, 2,516 downloads/month

  • 2019 – reg (Image registration) URL: https://neurodata.io/reg/

    A Python package which performs non-linear affine and deformable image registration

    6 stars, 4 forks, 61 downloads/month

  • 2019 – neuroparc URL: https://github.com/neurodata/neuroparc

    This repository contains a number of useful parcellations, templates, masks, and transforms to (and from) MNI152NLin6 space. The files are named according to the BIDs specification

    26 stars, 4 forks

  • 2019 – https://neurodata.io/forests/ URL: Sparse Projection Oblique Randomer Forests (Classification and regression)

    SPORF is an improved random forest algorithm that achieves better accuracy and scaling than previous implementations on a standard suite of > 100 benchmark problems

    54 stars, 35 forks, 73 downloads/month, 36 docker pulls

  • 2019 – Uncertainty-Forest URL: https://github.com/neurodata/uncertainty-forest

    A Python package containing estimation procedures for posterior distributions, conditional entropy, and mutual information between random variables X and Y

    2 stars, 1 fork

  • 2018 – LOL (Supervised dimensionality reduction) URL: https://neurodata.io/lol/

    Linear Optimal Low-rank (LOL) projection for improved classification performance in high-dimensional classification tasks

    8 stars, 6 forks, 60 downloads/month

  • 2018 – MGC (Non-parametric hypothesis testing) URL: https://neurodata.io/mgc/

    Multiscale Graph Correlation (MGC) is a framework for universally consistent testing high-dimensional and non-Euclidean data

    28 stars, 11 forks, 120 downloads/month, 266 docker pulls

  • 2018 – m2g (MR graph analysis) URL: https://neurodata.io/m2g/

    A Python pipeline which uses diffusion MRI data from individuals to generate connectomes reliably and scalably

    35 stars, 26 forks, 218 downloads/month, 7,900 docker pulls

  • 2018 – ndcloud (NeuroData Cloud) URL: https://neurodata.io/nd_cloud/

    The deployment of tools which support the Open Connectome Project

  • 2016 – Non-Parametric-Clustering URL: https://github.com/neurodata/non-parametric-clustering

    A program which uses non-parametric-clustering to minimize or maximize a given criterion function

    3 stars, 2 forks

Open-source Software: Contributed

Open-source Software: Archived

  • 2017 – 2019 ndex URL: https://github.com/neurodata/ndex

    Python 3 command-line program to exchange (download/upload) image data with NeuroData's cloud deployment of APL's BOSS spatial database

    3 stars, 0 forks, 89 downloads/month

  • 2017 – 2019 knor (Clustering) URL: https://github.com/flashxio/knorPy

    Python version of knor, a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access (NUMA) architectures

    1 stars, 3 forks, 115 downloads/month

  • 2017 – 2019 SynapseAnalysis (Synapse Detection) URL: https://github.com/aksimhal/SynapseAnalysis

    A framework to evaluate synaptic antibodies for array tomography applications

    2 stars, 0 forks

  • 2017 – 2018 MEDA (Matrix Exploratory Data Analysis) URL: https://github.com/neurodata/pymeda

    A python package for matrix exploratory data analysis

    0 stars, 3 forks, 56 downloads/month, 21 docker pulls

  • 2017 – 2018 ndwebtools URL: https://github.com/neurodata/ndwebtools

    ndwebtools (ndweb) is a Django application to provide a user-friendly interface for interacting with NeuroData resources and data

    0 stars, 1 forks

  • 2015 – 2018 ndviz URL: https://github.com/neurodata/ndviz

    Web visualization and analysis tools for neuroimaging datasets, powered by Neuroglancer

    8 stars, 4 forks, 48 docker pulls

  • 2015 – 2016 DMG URL: https://github.com/mkazhdan/DMG

    An implementation of a distributed multigrid Poisson solver for image stitching, smoothing, and sharpenting

    19 stars, 6 forks

  • 2015 VESICLE (EM Synapse Detection) URL: https://github.com/neurodata/vesicle

    Reference synapse detection program for processing serial electron microscopy data

    3 stars, 3 forks

  • 2015 CAJAL URL: https://github.com/neurodata/CAJAL

    A MATLAB API that provides a simple to use interface with Open Connectome Project servers and provides RAMON Objects, unit tests, configuration scripts, and utilities

    6 stars, 5 forks

  • 2012 – 2017 FlashGraph (Scalable Analytics) URL: https://github.com/flashxio/FlashX

    General-purpose graph analysis framework that exposes vertex-centric programming interface for users to express varieties of graph algorithms

    220 stars, 42 forks

  • 2012 – 2017 FlashX (Scalable machine learning) URL: https://github.com/flashxio/FlashX

    A matrix computation engine that provides a small set of generalized matrix operations on sparse matrices and dense matrices to express varieties of data mining and machine learning algorithms

    220 stars, 42 forks

  • 2011 – 2016 oopsi (Calcium Spike Sorting) URL: https://github.com/jovo/oopsi

    Model-based spike train inference from calcium imaging

    20 stars, 9 forks

  • 2011 – 2017 ndstore URL: https://github.com/neurodata/ndstore

    Scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data

    37 stars, 13 forks

Consultancy

Advisory Board Appointments

  • 2018 – Advisory Board Mind-X URL: https://mind-x.io/

    A neurotechnology company combining brain-computer interfaces and artificial intelligence to make the world’s information available with the speed and ease of a single thought. Incubated at Camden Partners Nexus, completed an initial round of funding for an undisclosed amount. 15 employees.

  • 2017 – Advisory Board PivotalPath URL: https://www.pivotalpath.com/

    PivotalPath is a leading hedge fund research and intelligence organization built by a team of experienced alternative investment professionals and fintech developers. Raised undisclosed amount of funding, 11 employees.

Startups

  • 2017 Co-Founder gigantum URL: http://gigantum.io

    The future of data science is open, decentralized and user friendly. That is why we created a platform that enables anybody to create and share totally reproducible computational work with the world. Completed initial round of seed funding for undisclosed amount from <a href="https://www.digital-science.com/">Digital Science</a>, which also funds figshare, readcube, altmetric, overleaf, and more.

    15 employees.

  • 2016 Co-Founder d8alab URL: http://www.d8alab.com

    Our services include evaluating model performance, building prototype R/Shiny web applications and basic data cleaning., Provides data science consulting for a variety of companies, specifically biomedical data science

    4 employees.