The Statistical Methods in Imaging conference is the annual meeting of the ASA Statistics in Imaging section. The Conference aims at gathering investigators working on methods and applications in imaging science.
The conference will be conducted over three days.
The first day will feature a R Software Development Workshop (link).
Oral, poster presentations and collaborative case study presentations will be featured during the conference.
The abstract submission for poster presentations is now open!
Martin A. Lindquist (Professor of Biostatistics, Johns Hopkins University)
“How to lie with fMRI”
The collaborative case-studies continue the long-standing tradition of the conference to foster translational communications between statisticians and their field collaborators, by discussing how a particular applied problem has been addressed with innovative statistical methodology.
For the SMI 2019, we have organized 3 collaborative case studies, led by
- John Kornak (UCSF, Biostatistics), Adam Staffaroni (UCSF, Neurology)
- Dan Gillen (UCI, Statistics), Mike Yassa (UCI, Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory), Nick Tustison (University of Virginia, Radiology and Medical Imaging), and Andrew Holbrook (UCLA Statistics)
- Wes Thompson (UCSD, Biostatistics)
Other Confirmed Participants
- Nichole Carlson, University of Colorado Denver
- Mark Fiecas, University of Minnesota
- Ying Guo, Emory University
- Jarek Harezlak, Indiana University
“Matrix-variate regression methods: SpINNEr to the rescue”
- Brian Hobbs, Cleveland Clinic
“A Bayesian Nonparametric approach for Cancer Radiomics: elucidating textural pattern heterogeneity of solid lesions”
- Jian Kang, University of Michigan
- Seonjoo Lee, Columbia University
- Lexin Li, UC Berkeley
“Mixed-effect time-varying stochastic blockmodel and application in brain connectivity analysis”
- Amanda Meja, Indiana University
“Fast spatial Bayesian modeling of cortical surface task activation”
- Todd Ogden, Columbia University
- Raquel Prado, UC Santa Cruz,
“Recent Bayesian Approaches for Analysis of Neuroimaging Data”
- Damla Senturk, UC Los Angeles
“Covariate-Adjusted Region-Referenced Generalized Functional Linear Model for EEG Data”
- Armin Schwartzman, UC San Diego
“Do not test for activation in fMRI but estimate the regions of activation“
- Simon Vandekar, Vanderbilt Universit
“Robust Spatial Extent Inference with a Semiparametric Bootstrap Joint Testing Procedure”
- Marina Vannucci, Rice University
“Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer’s Disease “
For all inquiries, please contact us smi2019uci at gmail.com