Program


A preliminary schedule for the conference can be found here.

 

Keynote Speakers

Tom Nichols (Professor of Neuroimaging Statistics, Nuffield Department of Population Health, University of Oxford)

Talk Title: Statistical Challenges and Opportunities in Population Neuroimaging

Vince Calhoun (Director, Tri-institutional Center for Translational Research in Neuroimaging and Data Science)


Short Course

Introduction to Deep Learning (8:30am-12:30pm on 05/18/2020)

Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing. This half-day workshop will introduce the fundamentals of the main types of deep learning models.  You will also learn the motivation and use cases of deep learning through hands-on exercises using R and Python in the cloud environment. This workshop is designed for the audience with a statistics background. No software download or installation is needed, everything is done through an internet browser (Chrome or Firefox) in Databricks free cloud environment.

Topics:

  •       Feedforward Neural Networks
  •       Convolutional Neural Networks
  •       Recurrent Neural Networks
  •       Deep Learning Hands-on (Python and R)

Instructor: Hui Lin, Head of Data Science at Netlify, 2325 3rd street, Suite 215, San Francisco, CA, 94017, hui [at] netlify [dot] com

Bio: Hui Lin is the head of data science at Netlify where she is leading and building the data science department. Before Netlify, she was a Data Scientist at DuPont. She provided data science leadership for a broad range of predictive analytics and market research analysis from 2013 to 2018. She is the co-founder of Central Iowa R User Group, blogger of https://scientistcafe.com/, and 2018 Program Chair of ASA Statistics in Marketing Section. She enjoys making analytics accessible to a broad audience and teaches tutorials and workshops for practitioners on data science (https://course2019.scientistcafe.com/). She holds an MS and Ph.D. in statistics from Iowa State University.


Statistical Software for Imaging Analysis

Efstathios D. Gennatas, Stanford University
“Efficient and accessible Machine Learning with rtemis”

Joshua Lukemire, Emory University
“HINT – A Matlab toolbox for hierarchical covariate-adjusted independent component analysis of fMRI data”

John Muschelli, Johns Hopkins Bloomberg School of Public Health
“Neuroconductor: An R Platform for Medical Imaging Analysis”

Hernando Ombao, King Abdullah University of Science and Technology
“Modeling and Visualization of Connectivity in EEG”

Marina Vannucci, Rice University
“User-friendly MATLAB GUIs for Bayesian Multi-Subject Modeling of fMRI Data”


Speakers in Invited Oral Sessions

Jesus Arroyo, Johns Hopkins University
“Inference for multiple heterogeneous networks with a common invariant subspace”

Joanne Beer, University of Pennsylvania
“Harmonization of longitudinal multi-scanner MRI neuroimaging data with application to the ADNI dataset”

Brian Caffo, Johns Hopkins University
“Covariance regression for connectome outcomes”

Guanqun Cao, Auburn University
“Empirical Likelihood for varying coefficient Geo Models”

Gang Chen, NIMH
“Can we do better than thresholding and discretization in network modeling?”

Shuo Chen, University of Maryland
“l_0 shrinkage in graph space for brain network inference”

Adam Ciarleglio, George Washington University
“Multiple imputation in functional regression with applications to EEG data in a depression study”

Ciprian Crainiceanu, Johns Hopkins University
“Methods for brain white matter segmentation in Alzheimer Disease”

Leo Duan, University of Florida
“Interpretable Dimension Reduction of Images via Random Transport”

Ani Eloyan, Brown University
“Analysis of structural imaging data in cancer”

Sharmistha Guha, Duke University
“Bayesian regression with undirected network predictors with an application to brain connectome data”

Michele Guindani, University of California
“A Bayesian Nonparametric approach for the analysis of functional data in neuroimaging”

Jaroslaw Harezlak, Indiana University
“Incorporation of structural and functional connectivity in regularized regression models”

Chao Huang, Florida State University
“Shape-on-Vector Geodesic Regression Model and Its Applications in Image Data Analysis”

Jian Kang, University of Michigan
“Interpretable machine learning methods for brain-computer interface”

John Kornak, University of California San Francisco
“Bayesian Image analysis in wavelet and other transformed spaces”

Robert T. Krafty, University of Pittsburgh
“Adaptive Spectral Analysis of High-Dimensional EEG with Application to Monitoring Transcranial Magnetic Stimulation during Psychosis”

Sebastian Kurtek, Ohio State University
“Visualization and Outlier Detection for Shape Data”

Nicole Lazar, University of Georgia
“Topological Data Analysis for the Study of Brain Networks”

Kristin Linn, University of Pennsylvania
“Inter-modal Coupling for Multi-modal Image Analysis”

Xi Luo, The University of Texas
“Covariate Assisted Principal Regression for Covariance Matrix Outcomes with an Application to fMRI”

Amanda Mejia, Indiana University
“Template ICA : Accurate estimation of subject-level resting-state networks through a hierarchical Bayesian ICA model with empirical group priors”

Michelle F. Miranda, University of Victoria
“A composite-hybrid basis model approach with applications in neuroimaging data”

Shariq Mohammed, University of Michigan
“Radiogenomic Analysis Incorporating Tumor Heterogeneity in Imaging through Densities”

Jeff Morris, The University of Texas MD Anderson Cancer Center 
“Bayesian Approaches for Functional Data Analysis with Imaging Applications”

Manjari Narayan, Stanford University
“Operating Characteristics of Network Centrality with applications to Network Neuroscience”

Todd Ogden, Columbia University
“Constrained functional additive models for interaction effects between a treatment and functional covariates”

Hernando Ombao, King Abdullah University of Science and Technology (KAUST)
“Stochastic Processes with Non-Linear Cross-Frequency Interactions with Applications to EEGs and LFPs”

Annie Qu, University of California
“Correlation Tensor Decomposition and Its Application in Spatial Imaging”

Russell Shinohara, University of Pennsylvania
“Statistical Challenges in Disentangling Heterogeneity of Multiple Sclerosis Lesions”

Haochang Shou, University of Pennsylvania
“Correcting Site Differences in the Covariance Structures of Neuroimaging to Improve Multivariate Pattern Analysis”

Dana Tudorascu, University of Pittsburgh
“Methods for MRI and PET neuroimaging data harmonization across different scanners in Alzheimer Disease”

Marina Vannucci, Rice University
“Semiparametric Bayesian Inference for Stationary Points in GaussianProcess Regression Models”

Julia Wrobel, University of Colorado
“Intensity Warping for Multisite MRI Harmonization”

Fengqing (Zoe) Zhang, Drexel University
“Penalized multi-state models for examining multimodal imaging signatures of Alzheimer’s disease”

Zhengwu Zhang, University of Rochester
“Surface-Based Connectivity Integration”

Yi Zhao, Indiana University-Purdue University
“Multimodal neuroimaging data integration and pathway analysis”

Yize Zhao, Yale University
“Genetic influences on brain structural connectivity under Bayesian shrinkage”

 

Speakers in Collaborative Case-Studies

Candace Fleischer, Emory University and Georgia Tech
“In vivo metabolic imaging with magnetic resonance spectroscopy”

Susan Gauthier, Weill Cornell Medicine
“The clinical translation of QSM as a new imaging biomarker for disease progression and treatment response in Multiple Sclerosis”

David Reiter, Emory University
“Advances in Quantitative MRI Signal Modeling in Musculoskeletal Research”

Deqiang Qiu, Emory University and Georgia Tech
“Quantitative susceptibility mapping and neurological applications”

Sandra Hurtado Rúa, Cleveland State University
“Statistical challenges in the analysis of QSM maps:Multiplicity and Causal inference”

Elizabeth Sweeney, Weill Cornell Medicine
“QSM Image Analysis: Automated Lesion Type Identification and Lesion Age Estimation”