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Computer Science

D-Index
53
Citations
15045
World Ranking
4727
National Ranking
2198

Overview

Joshua T. Vogelstein is affiliated with Johns Hopkins University in the United States. Their research spans neuroscience and medicine, with a significant focus on cognitive neuroscience, artificial intelligence, and advanced imaging techniques. Vogelstein's scholarly work involves intersecting fields including radiology, nuclear medicine, molecular biology, and statistics and probability.

Their main topics of study cover a broad range of areas within neuroscience and medical imaging. These include:

  • Functional Brain Connectivity Studies
  • Advanced Neuroimaging Techniques and Applications
  • Advanced MRI Techniques and Applications
  • Neural dynamics and brain function
  • Cell Image Analysis Techniques
  • Complex Network Analysis Techniques
  • COVID-19 Clinical Research Studies

Vogelstein has published frequently in venues such as bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), NeuroImage, Nature Communications, and eLife.

The scientist's recent published papers include:

  • "The connectome of an insect brain", 2023, Science
  • "Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets", 2020, Nature Communications
  • "Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish", 2023, Frontiers in Science
  • "Biological underpinnings for lifelong learning machines", 2022, Nature Machine Intelligence
  • "Toward a connectivity gradient-based framework for reproducible biomarker discovery", 2020, NeuroImage

Among Vogelstein's frequent co-authors are Carey E. Priebe, Eric Bridgeford, Michael P. Milham, Michael Powell, and Brian Caffo. These collaborations reflect an interdisciplinary approach to research intersecting neuroinformatics, machine learning, and brain connectivity.

Best Publications

  • Detection and localization of surgically resectable cancers with a multi-analyte blood test

    Joshua D. Cohen;Lu Li;Yuxuan Wang;Christopher Thoburn

  • Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex.

    Sonja B Hofer;Ho Ko;Bruno Pichler;Bruno Pichler;Joshua T Vogelstein

  • Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging

    Joshua T. Vogelstein;Adam M. Packer;Timothy A. Machado;Tanya Sippy

  • Imaging human connectomes at the macroscale

    R Cameron Craddock;Saad Jbabdi;Chao-Gan Yan;Chao-Gan Yan;Chao-Gan Yan;Joshua T Vogelstein

  • Fast non-negative deconvolution for spike train inference from population calcium imaging

    Joshua T. Vogelstein;Adam M. Packer;Tim A. Machado;Tanya Sippy

  • Whole-brain serial-section electron microscopy in larval zebrafish

    David Grant Colburn Hildebrand;Marcelo Cicconet;Russel M. Iguel Torres;Russel M. Iguel Torres;Woohyuk Choi

  • Spike Inference from Calcium Imaging Using Sequential Monte Carlo Methods

    Joshua T. Vogelstein;Brendon O. Watson;Adam M. Packer;Rafael Yuste;Rafael Yuste

  • Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning

    Joshua T. Vogelstein;Youngser Park;Tomoko Ohyama;Rex A. Kerr

  • A new look at state-space models for neural data

    Liam Paninski;Yashar Ahmadian;Daniel Gil Ferreira;Shinsuke Koyama

  • Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses

    R.J. Vogelstein;U. Mallik;J.T. Vogelstein;G. Cauwenberghs

  • Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets.

    Marc-Andre Schulz;B. T. Thomas Yeo;Joshua T. Vogelstein;Janaina Mourao-Miranada

  • DELTACON: A Principled Massive-Graph Similarity Function

    Unknown

  • Cross-species functional alignment reveals evolutionary hierarchy within the connectome.

    Ting Xu;Karl Heinz Nenning;Ernst Schwartz;Seok Jun Hong

  • FlashGraph: processing billion-node graphs on an array of commodity SSDs

    Da Zheng;Disa Mhembere;Randal Burns;Joshua Vogelstein

  • DELTACON: A principled massive-graph similarity function

    Danai Koutra;Joshua T. Vogelstein;Christos Faloutsos

  • Toward a connectivity gradient-based framework for reproducible biomarker discovery.

    Seok-Jun Hong;Ting Xu;Aki Nikolaidis;Jonathan Smallwood

  • Fast approximate quadratic programming for graph matching.

    Joshua T. Vogelstein;John M. Conroy;Vince Lyzinski;Louis J. Podrazik

  • Covariate-assisted spectral clustering

    Norbert Binkiewicz;Joshua T. Vogelstein;Karl Rohe

  • Toward Neurosubtypes in Autism.

    Seok-Jun Hong;Joshua T. Vogelstein;Alessandro Gozzi;Boris C. Bernhardt

  • DeltaCon: Principled Massive-Graph Similarity Function with Attribution

    Danai Koutra;Neil Shah;Joshua T. Vogelstein;Brian Gallagher

  • Statistical inference on random dot product graphs: a survey

    Avanti Athreya;Donniell E. Fishkind;Minh Tang;Carey E. Priebe

  • Graph Matching: Relax at Your Own Risk

    Vince Lyzinski;Donniell E. Fishkind;Marcelo Fiori;Joshua T. Vogelstein

  • Low-cost electroencephalogram (EEG) based authentication

    C. Ashby;A. Bhatia;F. Tenore;J. Vogelstein

Frequent Co-Authors

Carey E. Priebe
Carey E. Priebe Johns Hopkins University
Randal Burns
Randal Burns Johns Hopkins University
Michael P. Milham
Michael P. Milham Child Mind Institute
Ting Xu
Ting Xu University of California, Davis
Bert Vogelstein
Bert Vogelstein Johns Hopkins University
Mauro Maggioni
Mauro Maggioni Johns Hopkins University
Brett D. Mensh
Brett D. Mensh Howard Hughes Medical Institute
Karl Deisseroth
Karl Deisseroth Stanford University
David B. Dunson
David B. Dunson Duke University
Eric S. Perlman
Eric S. Perlman Florida Institute of Technology

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