Johns Hopkins Biomedical Engineering primary faculty
Joshua T. Vogelstein, PhD
Appointments: Institute for Computational Medicine
Center for Imaging Science
Institute for Data Intensive Engineering and Sciences
Office: Clark 317C
Lab: Joshua T. Vogelstein Website
PhD, Neuroscience, Johns Hopkins University, 2009
MS, Applied Mathematics & Statistics, Johns Hopkins University, 2009
BA, Biomedical Engineering, Washington University, 2002
This world is already an incredibly beautiful and lovely place to live (at least for me, for now). We are motivated by a desire to make it even better for all of us, as well as our descendants, and our fellow inhabitants. We do so via searching for patterns. Specifically, we seek patterns in our physical worlds (e.g., our bodies, our brains) as well as our mental worlds (e.g., our perceptions, experiences, memories, thoughts, emotions, psychiatric conditions). More importantly, we seek to understand patterns in our mental worlds in terms of our physical worlds. Our hope and belief is that via developing a deeper understanding of the links between these worlds, we will be able to bring them into greater alignment. A primary motivating factor is that all humans/animals have brains and therefore, such ideas could directly benefit all of animalkind. Thus, all of our research products are freely available to all.
JT Vogelstein, Y Park, T Ohyama, R Kerr, JW Truman, CE Priebe, M Zlatic. Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning. Science. 344, 286 (2014). website, abstract, pdf, reuters, code, data, DOI: 10.1126/science.1250298.
JT Vogelstein, WR Gray, RJ Vogelstein, CE Priebe. Graph Classification using Signal Subgraphs: Applications in Statistical Connectomics. IEEE TPAMI, vol. 35, no. 7, pp. 1539-1551, July 2013. arxiv, code, repo, article.
RC Craddock, S Jbabdi, C-G Yan, JT Vogelstein, FX Castellanos, A Di Martino, C Kelly, K Heberlein, S Colcombe, MP Milham. Imaging human connectomes at the macroscale. Nat Methods. Jun;10(6):524-39. 2013. doi: 10.1038/nmeth.2482. PMID 23722212. abstract, pdf.