Joshua Vogelstein

Last updated
Joshua T. Vogelstein
Vogelstein joshua v2.jpg
Vogelstein c.2014
Born
Joshua T. Vogelstein

1980 (age 4344)
United States
Parent Bert Vogelstein
Alma mater Washington University in St. Louis
Johns Hopkins University
Known for Connectomics, Graph theory
SpouseKathryn Vogelstein
Children3
Awards F1000 Prime Recommended (2014), [1]

Spotlight, Neural Information Processing Systems (2013),

Spotlight, Computational and Systems Neuroscience (2008)

Contents


Scientific career
Institutions Johns Hopkins University
Biomedical Engineering
Thesis OOPSI: A family of optimal optical spike inference algorithms for inferring neural connectivity from population calcium imaging  (2009)
Doctoral advisor Eric Young
Other academic advisorsCarey Priebe
Website neurodata.io , jovo.me

Joshua T. Vogelstein is an American biomedical engineer. He is an Associate Professor of Biomedical Engineering at Johns Hopkins University, where he sits at the Center for Imaging Science. Vogelstein also holds joint appointments in the departments of Applied Mathematics and Statistics, Computer Science, Electrical and Computer Engineering, Biostatistics, and Neuroscience. He has appointments in the Institute for Data Intensive Engineering and Sciences, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and the Mathematical Institute for Data Science.

His research focuses primarily on the intersection of natural and artificial intelligence. His group develops and applies high-dimensional nonlinear machine learning methods to biomedical data science challenges. They have published over 100 papers in prominent scientific and engineering journals and conferences including Nature, Science, PNAS, Neurips, and JMLR, with over 10,000 citations and an h-index over 40. They received funding from the Transformative Research Award from NIH, the NSF CAREER award, Microsoft Research, and many other government, for-profit and nonprofit organizations. He has advised over 60 trainees, and taught about 200 students in his eight years as faculty. In addition to his academic work, he co-founded Global Domain Partners, a quantitative hedge fund that was acquired by Mosaic Investment Partners in 2012, and software startup Gigantum, which was acquired by nVidia in early 2022. [2]

Academic background

Vogelstein did his undergraduate studies at McKelvey School of Engineering at Washington University in St. Louis, where he received his Bachelor of Science degree in biomedical engineering in 2002. From 2003 to 2009 he studied at Johns Hopkins University, where he received his Master of Science in Applied Mathematics and Statistics and a Ph.D. in Neuroscience from the Johns Hopkins School of Medicine, where he developed algorithms for spike detection in calcium imaging.

From 2014 to 2018, Vogelstein was the director of undergraduate studies for the Institute for Computational Medicine. He has also held positions as an endeavor scientist at the Child Mind Institute, a senior research scientist for the departments of statistical sciences and mathematics and neurobiology at Johns Hopkins University, and as affiliated faculty at Duke University.

Research

Vogelstein's research focuses on understanding how massive biomedical datasets are analyzed to discover new knowledge about the function of living systems in health and disease, and how this knowledge can be harnessed to provide improved, more affordable health care. Specifically, his work often focuses on big and wide data in neuroscience, and in particular on the statistics of brain graphs and connectomics.

Open Science, Open Data

Joshua Vogelstein founded and directs the NeuroData lab, which has created an ecosystem of open-source tools for neuroscientists and hosts a collection of open-source data. [3]

Network Statistics and Connectomics

Vogelstein's research has focused on connectal coding, [4] an emerging field focusing on the study of how brain structure, rather than brain activity, encodes information; this represents a shift from the traditional study of neural coding. Some of his notable work in this area includes the analysis of the first connectome of an insect brain (that of a Drosophila larva) [5] and his use of machine learning techniques to reveal patterns in larval Drosophila behaviors. [6]

He also studies brain connectivity at the mesoscale, [7] helping develop tools to study how neurons project across entire mammalian brains. [8] [9]

Motivated by this work on connectomes, Vogelstein has also developed statistical and computational methods for networks, including network statistical models, [10] network embedding methods, [11] [12] and methods for comparing [13] and matching [14] networks.

Artificial Intelligence

Dr. Vogelstein studies various out-of-distribution learning paradigms including meta, transfer, lifelong or continual learning, prospective learning both theoretically and practically. Motivated from his insight from brain study, he aims at making artificial intelligence better, more natural and complementary to human intelligence.

Vogelstein's research use domain knowledge from the fields of neuroscience and machine learning synergistically, aiming to gain deeper insights into natural intelligences (including organisms with brain) as well as building more robust and flexible artificial intelligences. Some key attributes of intelligence that he has researched on include representation capacity [15] and learning efficiency. [16]

Industry

Joshua Vogelstein has been on the advisory board for numerous commercial companies, including Gigantum, Mind-X, and PivotalPath. Vogelstein has also held the position of Chief Data Scientist at Global Domain Partners, LLC. [17] He works extensively with Microsoft Research.

Related Research Articles

Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

The Blue Brain Project is a Swiss brain research initiative that aims to create a digital reconstruction of the mouse brain. The project was founded in May 2005 by the Brain Mind Institute of École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Its mission is to use biologically-detailed digital reconstructions and simulations of the mammalian brain to identify the fundamental principles of brain structure and function.

Brain mapping is a set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the brain resulting in maps.

<span class="mw-page-title-main">Connectome</span> Comprehensive map of neural connections in the brain

A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". An organism's nervous system is made up of neurons which communicate through synapses. A connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

The Human Connectome Project (HCP) is a five-year project sponsored by sixteen components of the National Institutes of Health, split between two consortia of research institutions. The project was launched in July 2009 as the first of three Grand Challenges of the NIH's Blueprint for Neuroscience Research. On September 15, 2010, the NIH announced that it would award two grants: $30 million over five years to a consortium led by Washington University in St. Louis and the University of Minnesota, with strong contributions from University of Oxford (FMRIB) and $8.5 million over three years to a consortium led by Harvard University, Massachusetts General Hospital and the University of California Los Angeles.

Connectograms are graphical representations of connectomics, the field of study dedicated to mapping and interpreting all of the white matter fiber connections in the human brain. These circular graphs based on diffusion MRI data utilize graph theory to demonstrate the white matter connections and cortical characteristics for single structures, single subjects, or populations.

A brain atlas is composed of serial sections along different anatomical planes of the healthy or diseased developing or adult animal or human brain where each relevant brain structure is assigned a number of coordinates to define its outline or volume. Brain atlases are contiguous, comprehensive results of visual brain mapping and may include anatomical, genetic or functional features. A functional brain atlas is made up of regions of interest, where these regions are typically defined as spatially contiguous and functionally coherent patches of gray matter.

<span class="mw-page-title-main">Budapest Reference Connectome</span>

The Budapest Reference Connectome server computes the frequently appearing anatomical brain connections of 418 healthy subjects. It has been prepared from diffusion MRI datasets of the Human Connectome Project into a reference connectome, which can be downloaded in CSV and GraphML formats and visualized on the site in 3D.

<span class="mw-page-title-main">Large-scale brain network</span> Collections of brain regions

Large-scale brain networks are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.

<span class="mw-page-title-main">Russell Poldrack</span>

Russell "Russ" Alan Poldrack is an American psychologist and neuroscientist. He is a professor of psychology at Stanford University, associate director of Stanford Data Science, member of the Stanford Neuroscience Institute and director of the Stanford Center for Reproducible Neuroscience and the SDS Center for Open and Reproducible Science.

Viktor K. Jirsa is a German physicist and neuroscientist, director of research at the Centre national de la recherche scientifique (CNRS), director of the Institut de Neuroscience des Systèmes and co-director of the Fédération Hospitalo-Universitaire (FHU) EPINEXT "Epilepsy and Disorders of Neuronal Excitability" in Marseille, France. He is workpackage leader in the Epinov project funded in the context of the RHU3 call and coordinated by Fabrice Bartolomei.

<span class="mw-page-title-main">Residual neural network</span> Deep learning method

A residual neural network is a deep learning architecture in which the weight layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition and won that year's ImageNet Large Scale Visual Recognition Challenge.

Jeff W. Lichtman is an American neuroscientist. He is the Jeremy R. Knowles Professor of Molecular and Cellular Biology and Santiago Ramón y Cajal Professor of Arts and Sciences at Harvard University. He is best known for his pioneering work developing the neuroimaging connectomic technique known as Brainbow.

David C. Van Essen is an American neuroscientist specializing in neurobiology and studies the structure, function, development, connectivity and evolution of the cerebral cortex of humans and nonhuman relatives. After over two decades of teaching at the Washington University in St. Louis School of Medicine, he currently serves as an Alumni Endowed Professor of Neuroscience and maintains an active laboratory. Van Essen has held numerous positions, including Editor-in-Chief of the Journal of Neuroscience, Secretary of the Society for Neuroscience, and the President of the Society for Neuroscience from 2006 to 2007. Additionally, Van Essen has received numerous awards for his efforts in education and science, including the Krieg Cortical Discoverer Award from the Cajal Club in 2002, the Peter Raven Lifetime Achievement Award from St. Louis Academy of Science in 2007, and the Second Century Award in 2015 and the Distinguished Educator Award in 2017, both from Washington University School of Medicine.

Network neuroscience is an approach to understanding the structure and function of the human brain through an approach of network science, through the paradigm of graph theory. A network is a connection of many brain regions that interact with each other to give rise to a particular function. Network Neuroscience is a broad field that studies the brain in an integrative way by recording, analyzing, and mapping the brain in various ways. The field studies the brain at multiple scales of analysis to ultimately explain brain systems, behavior, and dysfunction of behavior in psychiatric and neurological diseases. Network neuroscience provides an important theoretical base for understanding neurobiological systems at multiple scales of analysis.

Marta Zlatic is a Croatian neuroscientist who is group leader at the MRC Laboratory of Molecular Biology in Cambridge, UK. Her research investigates how neural circuits generate behaviour.

<span class="mw-page-title-main">Dimitri Van De Ville</span> Swiss-Belgian computer scientist and neuroscientist specialized in brain activity networks

Dimitri Van De Ville is a Swiss and Belgian computer scientist and neuroscientist specialized in dynamical and network aspects of brain activity. He is a professor of bioengineering at EPFL and the head of the Medical Image Processing Laboratory at EPFL's School of Engineering.

References

  1. "NeuroData Awards".
  2. "Gigantum".
  3. Vogelstein, Joshua T.; Perlman, Eric; Falk, Benjamin; Baden, Alex; Gray Roncal, William; Chandrashekhar, Vikram; Collman, Forrest; Seshamani, Sharmishtaa; Patsolic, Jesse L.; Lillaney, Kunal; Kazhdan, Michael; Hider, Robert; Pryor, Derek; Matelsky, Jordan; Gion, Timothy; Manavalan, Priya; Wester, Brock; Chevillet, Mark; Trautman, Eric T.; Khairy, Khaled; Bridgeford, Eric; Kleissas, Dean M.; Tward, Daniel J.; Crow, Ailey K.; Hsueh, Brian; Wright, Matthew A.; Miller, Michael I.; Smith, Stephen J.; Vogelstein, R. Jacob; et al. (2019). "A community-developed open-source computational ecosystem for big neuro data". Nature Methods. 15 (11): 846–847. arXiv: 1804.02835 . doi:10.1038/s41592-018-0181-1. PMC   6481161 . PMID   30377345.
  4. Vogelstein, Joshua T; Bridgeford, Eric W; Pedigo, Benjamin D; Chung, Jaewon; Levin, Keith; Mensh, Brett; Priebe, Carey E (2019-04-01). "Connectal coding: discovering the structures linking cognitive phenotypes to individual histories" (PDF). Current Opinion in Neurobiology. Machine Learning, Big Data, and Neuroscience. 55: 199–212. doi: 10.1016/j.conb.2019.04.005 . ISSN   0959-4388. PMID   31102987.
  5. Winding, Michael; Pedigo, Benjamin D.; Barnes, Christopher L.; Patsolic, Heather G.; Park, Youngser; Kazimiers, Tom; Fushiki, Akira; Andrade, Ingrid V.; Khandelwal, Avinash; Valdes-Aleman, Javier; Li, Feng; Randel, Nadine; Barsotti, Elizabeth; Correia, Ana; Fetter, Richard D.; Hartenstein, Volker; Priebe, Carey E.; Vogelstein, Joshua T.; Cardona, Albert; Zlatic, Marta (2023-03-10). "The connectome of an insect brain". Science. 379 (6636): –9330. doi:10.1126/science.add9330. PMC   7614541 . PMID   36893230. S2CID   254070919.
  6. Vogelstein, Joshua T.; Park, Youngser; Ohyama, Tomoko; Kerr, Rex A.; Truman, James W.; Priebe, Carey E.; Zlatic, Marta (2014-04-25). "Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning". Science. 344 (6182): 386–392. Bibcode:2014Sci...344..386V. doi: 10.1126/science.1250298 . ISSN   0036-8075. PMID   24674869. S2CID   7404747.
  7. Zeng, Hongkui (2018). "Mesoscale Connectomics". Current Opinion in Neurobiology. 50: 154–162. doi:10.1016/j.conb.2018.03.003. ISSN   0959-4388. PMC   6027632 . PMID   29579713.
  8. Athey, Thomas L.; Tward, Daniel J.; Mueller, Ulrich; Vogelstein, Joshua T.; Miller, Michael I. (2022-04-25). "Hidden Markov modeling for maximum probability neuron reconstruction". Communications Biology. 5 (1): 388. doi:10.1038/s42003-022-03320-0. ISSN   2399-3642. PMC   9038756 . PMID   35468989.
  9. Athey, Thomas L.; Wright, Matthew A.; Pavlovic, Marija; Chandrashekhar, Vikram; Deisseroth, Karl; Miller, Michael I.; Vogelstein, Joshua T. (2023-03-01), "BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes", BioRxiv: The Preprint Server for Biology, bioRxiv, doi:10.1101/2023.02.28.530429, PMC   10002688 , PMID   36909631 , retrieved 2023-03-28
  10. Athreya, Avanti; Fishkind, Donniell E.; Tang, Minh; Priebe, Carey E.; Park, Youngser; Vogelstein, Joshua T.; Levin, Keith; Lyzinski, Vince; Qin, Yichen; Sussman, Daniel L. (2018). "Statistical Inference on Random Dot Product Graphs: a Survey". Journal of Machine Learning Research. 18 (226): 1–92. arXiv: 1709.05454 . ISSN   1533-7928 . Retrieved 2022-08-02.
  11. Arroyo, Jesús; Athreya, Avanti; Cape, Joshua; Chen, Guodong; Priebe, Carey E.; Vogelstein, Joshua T. (2021). "Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace". Journal of Machine Learning Research. 22 (142): 1–49. arXiv: 1906.10026 . ISSN   1533-7928. PMC   8513708 . PMID   34650343 . Retrieved 2023-03-28.
  12. Binkiewicz, N.; Vogelstein, J. T.; Rohe, K. (2017). "Covariate-assisted spectral clustering". Biometrika. 104 (2): 361–377. doi:10.1093/biomet/asx008. ISSN   1464-3510. PMC   5793492 . PMID   29430032.
  13. Koutra, Danai; Vogelstein, Joshua T.; Faloutsos, Christos (2013-05-02). "DeltaCon: A Principled Massive-Graph Similarity Function". Proceedings of the 2013 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics. pp. 162–170. arXiv: 1304.4657 . doi:10.1137/1.9781611972832.18. ISBN   978-1-61197-262-7. S2CID   5310840 . Retrieved 2023-03-28.
  14. Vogelstein, Joshua T.; Conroy, John M.; Lyzinski, Vince; Podrazik, Louis J.; Kratzer, Steven G.; Harley, Eric T.; Fishkind, Donniell E.; Vogelstein, R. Jacob; Priebe, Carey E. (2015). "Fast Approximate Quadratic programming for graph matching". PLOS ONE. 10 (4): e0121002. Bibcode:2015PLoSO..1021002V. doi: 10.1371/journal.pone.0121002 . ISSN   1932-6203. PMC   4401723 . PMID   25886624.
  15. Wang, Qingyang; Powell, Michael A.; Geisa, Ali; Bridgeford, Eric; Priebe, Carey E.; Vogelstein, Joshua T. (2023-03-08). "Why do networks have inhibitory/negative connections?". arXiv: 2208.03211 [cs.LG].
  16. Wang, Qingyang; Powell, Michael A.; Geisa, Ali; Bridgeford, Eric; Vogelstein, Joshua T. (2023-03-29). "Polarity is all you need to learn and transfer faster". arXiv: 2303.17589 [cs.LG].
  17. "global domain partners". Bloomberg News . 22 September 2023.