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 Workshop will be held in the Donald Bren Hall Conference Center
(Room DBH 6011, 6th floor)
in the Donald Bren Hall on UCI Campus
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!
Charles De Carli (Professor of Neurology, UC Davis)
Statistical Issues in Neuroimaging of Human Aging and the Transition to Dementia
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
“Radiomics of lung CT as a tool for developing disease phenotypes in lung disease”
- Mark Fiecas, University of Minnesota
“A Grouped Beta Process Model for Multivariate Resting-State EEG Microstate Analysis on Twins “
- Ying Guo, Emory University
“A Hierarchical Independent Component Analysis Method for Longitudinal Neuroimaging Studies”
- 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
“Bayesian network-on-scalar regression with application to neuroimaging data”
- 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
“Distance-based statistics in PET imaging”
- 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 University
“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