Dr. Calhoun received a bachelor’s degree in electrical engineering (EE) from the University of Kansas, Lawrence, Kansas in 1991, master’s degrees in biomedical engineering and information systems from Johns Hopkins University, Baltimore in 1993 and 1996, and the Ph.D. degree in EE from the University of Maryland Baltimore County, Baltimore in 2002. He worked as a research engineer in the psychiatric neuroimaging laboratory at Johns Hopkins from 1993 until 2002.
R. Jiang, C. C. Abbott, T. Jiang, Y. Du, R. Espinoza, K. L. Narr, B. Wade, Q. Yu, M. Song, D. Lin, J. Chen, T. Jones, M. Argyelan, G. Petrides, J. Sui, and V. D. Calhoun, "SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets," Neuropsychopharmacology, vol. 43, pp. 1078-1087, Apr. 2018, PMC5854791.
J. Sui, S. L. Qi, T. G. M. van Erp, J. Bustillo, R. T. Jiang, D. D. Lin, J. A. Turner, E. Damaraju, A. R. Mayer, Y. Cui, Z. N. Fu, Y. H. Du, J. Y. Chen, S. G. Potkin, A. Preda, D. H. Mathalon, J. M. Ford, J. Voyvodic, B. A. Mueller, A. Belger, S. C. McEwen, D. S. O'Leary, A. McMahon, T. Z. Jiang, and V. D. Calhoun, "Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion," Nature Communications, vol. 9, Aug. 2, 2018.
V. D. Calhoun and J. Sui, "Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness," Biol Psychiatry Cogn Neurosci Neuroimaging, vol. 1, pp. 230-244, May 2016, PMC4917230.
S. M. Plis, A. D. Sarwate, D. Wood, C. Dieringer, D. Landis, C. Reed, S. R. Panta, J. A. Turner, J. M. Shoemaker, K. W. Carter, P. Thompson, K. Hutchison, and V. D. Calhoun, "COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data," Front Neurosci, vol. 10, p. 365, Aug. 19, 2016, PMC4990563.
G. D. Pearlson, J. Liu, and V. D. Calhoun, "An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders," Front Genet, vol. 6, p. 276, 2015, PMC4561364.
J. Sui, G. D. Pearlson, Y. Du, Q. Yu, T. R. Jones, J. Chen, T. Jiang, J. Bustillo, and V. D. Calhoun, "In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia," Biol Psychiatry, vol. 78, pp. 794-804, Dec. 1, 2015, PMC4547923.
V. D. Calhoun, R. Miller, G. Pearlson, and T. Adali, "The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery," Neuron, vol. 84, pp. 262-274, Oct. 22, 2014, PMC4372723.
S. M. Plis, D. R. Hjelm, R. Salakhutdinov, E. A. Allen, H. J. Bockholt, J. D. Long, H. J. Johnson, J. S. Paulsen, J. A. Turner, and V. D. Calhoun, "Deep learning for neuroimaging: a validation study," Front Neurosci, vol. 8, p. 229, Aug. 20, 2014, 4138493.
S. A. Meda, B. Narayanan, J. Liu, N. I. Perrone-Bizzozero, M. C. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Saykin, and G. D. Pearlson, "A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort," Neuroimage, vol. 60, pp. 1608-1621, Apr. 15, 2012, PMC3312985.
V. D. Calhoun and T. Adali, "Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery," IEEE Rev Biomed Eng, vol. 5, pp. 60-73, 2012, PMC4433055.
V. D. Calhoun and T. Adalı, "Analysis of Complex-Valued Functional Magnetic Resonance Imaging Data: Are We Just Going Through a Phase?," Special Issue of the Bulletin of the Polish Academy of Sciences, vol. 60, pp. 371-418, 2012.
J. Sui, T. Adalı, Q. Yu, and V. D. Calhoun, "A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data," Journal of Neuroscience Methods, vol. 204, pp. 68-81, 2012, PMC3690333.
A. Scott, W. Courtney, D. Wood, R. de la Garza, S. Lane, M. King, R. Wang, J. Roberts, J. A. Turner, and V. D. Calhoun, "COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets," Front Neuroinform, vol. 5, p. 33, 2011, 3250631.
V. D. Calhoun and T. Adalı, "Feature-based Fusion of Medical Imaging Data," IEEE Transactions on Information Technology in Biomedicine, vol. 13, pp. 1-10, 2009, PMC2737598.
Yao Xie is an Assistant Professor and Harold R. and Mary Anne Nash Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011. Prior joining Georgia Tech in 2013, she worked as a Research Scientist at Duke University. Her research interests are statistics, signal processing, and machine learning.