1 Ani Eloyan Contact Information Department of Biostatistics Bloomberg School of Public Health Johns Hopkins University 615 North Wolfe Street, E3640 Baltimore, MD, Phone: Website: Education North Carolina State University, Raleigh, NC Ph.D., Statistics, 2010 Advisor: Sujit Ghosh Dissertation topic: Semi-Parametric Methods for Independent Component Analysis M.S., Statistics, 2007 Yerevan State University, Yerevan, Armenia B.S., Applied Mathematics and Computer Science, 2004 Professional Experience 08/ present, Assistant Professor Department of Biostatistics, Johns Hopkins University 10/ /2013, Postdoctoral Fellow Department of Biostatistics, Johns Hopkins University Mentor: Brian Caffo, Co-Mentor: Ciprian Crainiceanu 06/ /2010, Student Tester JMP Development and Testing, SAS institute Inc. 05/ /2008, Instructor Department of Statistics, North Carolina State University Academic Service Review of research proposals: NSF/Division of Mathematical Sciences, panelist, Statistics program. DOD/CDMRP/Investigator-Initiated Partnership Award, specialist reviewer - biostatistician, Multiple sclerosis research program.
2 University service: Johns Hopkins Biostatistics seminar coordinator ( ). Invited session organizer: Functional Neuroimaging Decompositions ENAR Session Chair: ENAR (2012) and JSM (2012, 2013, 2014). Membership: American Statistical Association, International Biometric Society/ENAR. Referee Service Honors and Awards Journal of Royal Statistical Society - Series B Journal of American Statistical Association (Theory and Methods) Annals of Applied Statistics Biostatistics Biometrics Technometrics Journal of Statistical Theory and Practice International Journal of Biostatistics Neuroimage Human Brain Mapping Frontiers in Systems Neuroscience Psychiatry Research: Neuroimaging Pharmacoepidemiology and Drug Safety Winner of the ADHD200 Global Competition, 2011 Developed prediction algorithms for 1) finding attention deficit hyperactivity disorder (ADHD) status in children using machine learning and 2) identifying disease biomarkers using functional magnetic resonance imaging. Elected to Phi Kappa Phi, 2006 Elected to Mu Sigma Rho, 2007 Publications Eloyan, A., Shou, H., Shinohara, R.T., Sweeney, E.M., Nebel, M.B., Cuzzocreo, J.L., Calabresi, P.A., Reich, D.S., Lindquist, M.A., and Crainiceanu, C.M. (2014) Health effects of lesion localization in multiple sclerosis: Spatial registration and confounding adjustment. PLoS One. 9, 9. doi: /journal.pone Cervenka, M.C, Patton, K., Eloyan, A., Henry, B., and Kossoff, E.H. (2014) Fasting lipid profiles of adults on the modified Atkins diet for epilepsy. Nutritional Neuroscience.
3 Shou, H., Eloyan, A., Nebel, M.B., Pekar, J.J., Mejia, A., Mostofsky, S.H., Caffo, B.S., Lindquist, M.A., and Crainiceanu, C.M. (2014) Shrinkage prediction of seed-voxel brain connectivity using resting state fmri. NeuroImage. 102, 2, Nebel, M.B., Eloyan, A., Barber, A.D., and Mostofsky, S.H. (2014) Precentral gyrus functional connectivity signatures of autism. Frontiers in Systems Neuroscience. 8, 80. doi: /fnsys Eloyan, A., Li, S., Muschelli, J., Pekar, J.J., Mostofsky, S.H., and Caffo, B.S. (2014) Analytic programming with fmri data: a quick-start guide for statisticians using R. PLoS One. 9, 2. doi: /journal.pone Risk, B.B., Matteson, D.S., Ruppert, D., Eloyan, A., and Caffo, B.S. (2014) An evaluation of independent component analyses with an application to resting-state fmri. Biometrics. 70, 1, Eloyan, A., Crainiceanu, C.M., and Caffo, B.S. (2013) Likelihood based population independent component analysis. Biostatistics. 14, 3, Shou, H., Eloyan, A., Lee, S., Zipunnikov, V., Crainiceanu, A.N., Nebel, M.B, Caffo, B.S., Lindquist, M.A., and Crainiceanu, C.M. (2013) Quantifying the reliability of image replication studies: The image intraclass correlation coefficient (I2C2). Cognitive, Affective, and Behavioral Neuroscience. 13, Eloyan, A. and Ghosh, S.K. (2013) A semiparametric approach to source separation using independent component analysis. Computational Statistics and Data Analysis. 58, Cervenka, M.C., Corines, J., Boatman-Reich, D., Eloyan, A., Sheng, X., Franaszczuk, P.J., and Crone, N.E. (2012) Electrocorticographic functional mapping identifies human cortex critical for auditory and visual naming. NeuroImage. 69, Li, S., Eloyan, A., Joel, S., Mostofsky S.H., Pekar, J.J., Basset, S.S., and Caffo, B.S. (2012) Analysis of group ICA-based connectivity measures from fmri: application to Alzheimer s Disease. PLoS One. 7, 11. doi: /journal.pone Eloyan, A., Muschelli, J., Nebel, M.B., Liu, H., Han, F., Zhao, T., Barber, A.D., Joel, S., Pekar, J.J., Mostofsky, S.H., and Caffo, B.S. (2012) Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Frontiers in Systems Neuroscience. 6, 61. doi: /fnsys Invited submission. Based on the winning entry of the ADHD 200 Prediction Competition. Eloyan, A. and Ghosh, S.K. (2011) Smooth density estimation with moment constraints using mixture distributions. Journal of Nonparametric Statistics. 23, 2, Hakopian, Y.R. and Eloyan, A.N. (2007) The Moore-Penrose inverse of block magic rectangles. International Journal of Mathematical Education in Science and Technology. 38, 8,
4 Hakopian, Y.R., Eloyan, A.N., and Khachatryan, D.E. (2006) About magic rectangles. International Journal of Mathematical Education in Science and Technology. 37, 4/15, Submitted Yang, J., Eloyan, A., Barber, A.D., Nebel, M.B., Mostofsky, S.H., Pekar, J.J., Crainiceanu, C.M., and Caffo, B.S. Homotopic group ICA for multi-subject brain imaging data. submitted. Nebel, M.B., Eloyan, A., Nettles, C., Ament, K., Sweeney, K., Ward, R., Barber, A.D., Choe. A., Pekar, J.J., and Mostofsky, S.H. Reduced intrinsic visual-motor synchrony relates to autism severity. submitted. Book Chapters Eloyan, A., Zipunnikov, V., Yang, J., and Caffo, B.S. Multivariate Decompositions in Brain Imaging. Handbook of Modern Statistical Methods: Neuroimaging Data Analysis. CRC Press, Sweeney, E.M., Eloyan, A., Shinohara, R.T., and Crainiceanu, C.M. The Statistical Analysis of Structural MRI Neuroimaging Data. Handbook of Modern Statistical Methods: Neuroimaging Data Analysis. CRC Press, Invited Presentations On Incorporating Imaging Data in Medical Records Analyses, Department of Health Care Policy, Harvard Medical School, Boston, MA, November, Sex differences in physical recovery after heart surgery: a minute-by-minute-by-year approach, GSA, Washington, DC, November Visual-motor connectivity in Autism via ICA, Department of Biostatistics Grand Rounds, Johns Hopkins University, November, Statistical Analysis of High Dimensional Neuroimaging Data, Biostatistics Division, Department of Child and Adolescent Psychiatry, New York University, NYC, NY, October, Matrix decompositions with applications in Neuroimaging, PenSIVE group, University of Pennsylvania, Philadelphia, PA, September, Challenges in statistical analysis of high-dimensional brain imaging data, JSM, Boston, MA, August, (Topic Contributed) Statistical Analysis of Imaging Data for Disease Exploration, WNAR, Honolulu, HI, June, The use of wearable technology for comparative effectiveness research, NESS, Boston, MA, April, 2014.
5 Statistical Analysis of High Dimensional Neuroimaging Data, Department of Statistics, University of Virginia, Charlottesville, VA, April, Matrix decomposition methods for functional MRI data. JSM, Montreal, Quebec, Canada, August, (Topic Contributed) Bayesian approaches for decompositions of neuroimaging data. ENAR, Orlando, FL, March, University of Pennsylvania, Philadelphia, PA, February, Johns Hopkins University, Baltimore, MD, February, George Washington University, Washington, DC, February, University of Massachusetts, Amherst, MA, January, Likelihood based population independent component analysis. Department of Statistics, Texas A&M University, College Station, TX, January, Likelihood based population independent component analysis. ENAR, Washington, DC, April, (Topic Contributed) Prediction methods for ADHD 200 competition. Frontiers in Addiction Research, Washington, DC, November, (Poster) Independent component analysis, application to EEG. Regional EEG/ERP Research Meeting, Baltimore, MD, April, Contributed Talks, Posters Likelihood based population independent component analysis, Hopkins Imaging Initiative, Baltimore, MD, October, (Poster) Bayesian independent component analysis for fmri Data, ENAR, Miami, FL, March, (Poster) Semiparametric approaches to separation of sources using independent component analysis, ENAR, New Orleans, LA, March, Bayesian independent component analysis using mixture priors, JSM, Washington DC, August, Smooth density estimation with moment constraints using mixture distributions, ENAR, San Antonio, TX, March, 2009.
6 Teaching Experience Advisees Qing Cai, PhD candidate (co-advisor, primary advisor Mei-Cheng Wang) James Pringle, ScM candidate (thesis reader, primary advisor Ciprian Crainiceanu) Junrui Di, PhD candidate (academic advisor) Instructor Advanced Special Topics in Biostatistics: Matrix Decompositions and Prediction, Applications in Neuroimaging (Spring 2014), Developed the course on the use of matrix decompositions and machine learning algorithms in neuroimaging data analysis. Introduction to Statistics (Summer 2007, Fall 2007, Spring 2008). Guest Lecturer Public Health Biostatistics (Fall 2014). Lab Instructor Statistical Methods in Public Health III (Spring 2012), Statistical Methods in Public Health I (Fall 2012), Experimental Statistics for Biological Sciences (Fall 2006, Spring 2007), Business Statistics (Fall 2005, Spring 2006). Personal Languages: English (fluent), Armenian (native), Russian (fluent),
Charlotte Chang, DrPH, MPH 2123 West Street, Berkeley, CA 94702 510-333-0089 firstname.lastname@example.org EDUCATION 2006 2010 DrPH University of California, Berkeley School of Public Health, Berkeley, CA Evaluation
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Jen-Mei Chang Research Interests Computational and applied mathematics. Geometric data and pattern analysis. Face and object recognition. Biometrics. Numerical methods for computing large data sets. Computer
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