Conference Schedule and Full Program

The conference program and session recordings can be accessed via the online program website.

Program at a Glance


Founders Talk

Timothy Johnson (Professor, Department of Biostatistics, University of Michigan)

Talk Title: A Bayesian semi-parametric model for functional near-infrared spectroscopy data


Keynote Speakers

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

Talk Title: Neuroimaging Statistics for Population Scale Data

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

Talk Title: Swimming in a sea of under-explored dynamics: A survey of approaches for capturing time-varying connectivity

Short Course

Introduction to Deep Learning 

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.


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

Instructor: Hui Lin, hui [at] linhui [dot] org

Bio: Hui Lin is currently a Quant Researcher at Google. Before Google, Hui held different roles in data science. She was the head of data science at Netlify, where she built and led the data science team, and a Data Scientist at DuPont, where she did a broad range of predictive analytics and market research analysis. She is the blogger of and the 2018 Program Chair of ASA Statistics in Marketing Section. She enjoys making analytics accessible to a broad audience and teaches tutorials and workshops for data science practitioners. She holds 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 University
“Neuroconductor: An R Platform for Medical Imaging Analysis”

Hernando Ombao, King Abdullah University of Science and Technology
“Toolbox for Exploring Interactions in Multivariate Time Series”

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

Speakers in Invited Oral Sessions

Jesus Arroyo, University of Maryland, College Park
“Inference for multiple heterogeneous networks with a common invariant subspace”

Joanne Beer, University of Pennsylvania
“Extensions of ComBat for harmonization of multi-scanner neuroimaging data in an Alzheimer’s disease Neuroimaging Initiative dataset”

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

Guanqun Cao, Auburn University
“Estimation of the Mean Function of Functional Data via Deep Neural Networks”

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
“Longitudinal Image Analysis and Inference”

Leo Duan, University of Florida
“Bayesian Vector Autoregression using the Tree Rank Prior with an Application to fMRI Data Analysis”

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
“Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity”

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

Emily Hector, North Carolina State University
“Integrative fused mean structure learning with application to image-on-scalar regression”

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

Jian Kang, University of Michigan
“Bayesian Inferences in EEG-Based Brain-Computer Interface via the Split-and-Merge Caussian Process”

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

Robert T. Krafty, Emory University
“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, Pennsylvania State University
“Topological Data Analysis for the Study of Brain Networks”

Lexin Li, University of California, Berkeley
“Testing Mediation Effects Using Logic of Boolean Matrices with Applications in Neuroimaging Mediation Analysis”

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, University of Pennsylvania
“Connectivity Regression”

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)
“Exploring Non-Linear Spectral Interactions in Multivariate Time Series”

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”

Sean Simpson, Wake Forest
“Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data”

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”

Xin Zhang, Florida State University
“Generalizing Liquid Association for Multimodal Neuroimaging” 

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”

Jingyi Zheng, Auburn University
“Time-frequency Spectral Analysis of Scalp EEG signals using Empirical Mode Decomposition” 

Speakers in Collaborative Case-Studies

Candace Fleischer, Emory University and Georgia Tech
“Metabolic and thermometric brain 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”