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Ciprian
M.
Crainiceanu
,
PhD

Professor
Ciprian Crainiceanu

Departmental Affiliations

Primary

Center & Institute Affiliations

Contact Info

615 N. Wolfe Street, Room E3636
Baltimore
Maryland
21205
US        

Research Interests

Biostatistics; Neuroimaging; Wearable computing; Activity; Sleep; Multiple Sclerosis; Multilevel (hierarchical) Bayesian Inference; MCMC; Longitudinal Modeling; Nonparametric Statistics; Smoothing; Measurement Error
Experiences & Accomplishments
Education
PhD
Cornell University
2003
MS
Cornell University
2002
MS
Cornell University
2002
Overview
My research is centered around Statistical methods for new technologies used in public health and medical studies. These technologies provide new types of data that are increasing both in size and complexity. I am interested in developing analytic tools that are tailored to specific applications, address the particular subtleties of the problem, and then find the common thread that eventually becomes Statistical methodology. My current scientific research interest centers around sleep research (EEG, polysomnograms), wearable computing (accelerometers, heart monitors), and multimodality brain imaging (SPECT, MRI, CT) with applications to Alzheimer, Multiple Sclerosis, traumatic brain injury, and cancer. My statistical expertise centers around inferential methods for ultra high dimensional data, mixed effects modeling, Bayesian inference, and smoothing.
Honors & Awards
Please see my CV for more details
Select Publications
Selection of recent papers
  • Measurement Error in Nonlinear Models (with R.J. Carroll, D. Ruppert, L.A. Stefanski), Second Edition, 2006
  • Methods in Biostatistics with R, https://leanpub.com/biostatmethods/
  • The upstrap. Crainiceanu CM, Crainiceanu A. Biostatistics. 2018
  • Neuroconductor: an R platform for medical imaging analysis. Muschelli J, Gherman A, Fortin JP, Avants B, Whitcher B, Clayden JD, Caffo BS, Crainiceanu CM. Biostatistics. 2018
  • Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis. Zipunnikov V, Greven S, Shou H, Caffo B, Reich DS, Crainiceanu C. Annals of Applied Statistics, 2014, 8(4):2175-2202