38 citations · 75 across the 20 of their papers we have counts for
23 papers
Robust Community Detection for Noisy Networks with Covariates: Application to Functional Brain Networks
Zeyu Hu, Frederick H. Xu, Suprateek Kundu +4
Community detection is fundamental to understanding the modular organization in functional brain networks, yet noise in neuroimaging-derived networks and auxiliary node-level covar…
Bayesian Scalar-on-Tensor Quantile Regression for Longitudinal Data on Alzheimer's Disease
Rongke Lyu, Marina Vannucci, Suprateek Kundu
As a general and robust alternative to traditional mean regression models, quantile regression avoids the assumption of normally distributed errors, making it a versatile choice wh…
DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning
Matthew Drexler, Benjamin Risk, James J Lah +2
Conventional multimodal data integration methods provide a comprehensive assessment of the shared or unique structure within each individual data type but suffer from several limit…
Flexible Bayesian Nonparametric Product Mixtures for Multi-scale Functional Clustering
Tsung-Hung Yao, Suprateek Kundu
There is a rich literature on clustering functional data with applications to time-series modeling, trajectory data, and even spatio-temporal applications. However, existing method…
Bayesian longitudinal tensor response regression for modeling neuroplasticity
Suprateek Kundu, Alec Reinhardt, Serena Song +4
A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxe…
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
Yang Li, Xin Ma, Raj Sunderraman +2
Neuroimaging-based prediction methods for intelligence and cognitive abilities have seen a rapid development in literature. Among different neuroimaging modalities, prediction base…
High-dimensional Measurement Error Models for Lipschitz Loss
Xin Ma, Suprateek Kundu
Recently emerging large-scale biomedical data pose exciting opportunities for scientific discoveries. However, the ultrahigh dimensionality and non-negligible measurement errors in…
Evaluating the Performance of StyleGAN2-ADA on Medical Images
McKell Woodland, John Wood, Brian M. Anderson +12
Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirem…
Flexible Bayesian Support Vector Machines for Brain Network-based Classification
Jin Ming, Suprateek Kundu
Objective: Brain networks have gained increasing recognition as potential biomarkers in mental health studies, but there are limited approaches that can leverage complex brain netw…
Flexible Bayesian Product Mixture Models for Vector Autoregressions
Suprateek Kundu, Joshua Lukemire
Bayesian non-parametric methods based on Dirichlet process mixtures have seen tremendous success in various domains and are appealing in being able to borrow information by cluster…
Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD
Yuexuan Wu, Suprateek Kundu, Jennifer S. Stevens +2
There is increasing evidence on the importance of brain morphology in predicting and classifying mental disorders. However, the vast majority of current shape approaches rely heavi…
Multi-task Learning with High-Dimensional Noisy Images
Xin Ma, Suprateek Kundu
Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image…
Integrative Learning for Population of Dynamic Networks with Covariates
Suprateek Kundu, Jin Ming, Joe Nocera +1
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to levera…
Semi-parametric Bayes Regression with Network Valued Covariates
Xin Ma, Suprateek Kundu, Jennifer Stevens
There is an increasing recognition of the role of brain networks as neuroimaging biomarkers in mental health and psychiatric studies. Our focus is posttraumatic stress disorder (PT…
Dynamic Brain Functional Networks Guided By Anatomical Knowledge
Suprateek Kundu, Jin Ming, Jennifer Stevens
Recently, the potential of dynamic brain networks as a neuroimaging biomarkers for mental illnesses is being increasingly recognized. However, there are several unmet challenges in…
A Differential Degree Test for Comparing Brain Networks
Ixavier A Higgins, Ying Guo, Suprateek Kundu +2
Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identi…
Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
Ixavier A. Higgins, Suprateek Kundu, Ying Guo
Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization. Specifically, accounting for knowledge of anatomical pathways conn…
Bayesian Joint Modeling of Multiple Brain Functional Networks
Joshua Lukemire, Suprateek Kundu, Giuseppe Pagnoni +1
Brain function is organized in coordinated modes of spatio-temporal activity (functional networks) exhibiting an intrinsic baseline structure with variations under different experi…
Scalable Bayesian Variable Selection for Structured High-dimensional Data
Changgee Chang, Suprateek Kundu, Qi Long
Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. Howev…
Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics
Suprateek Kundu, Minsuk Shin, Yichen Cheng +3
Significant advances in biotechnology have allowed for simultaneous measurement of molecular data points across multiple genomic and transcriptomic levels from a single tumor/cance…
Bayes Regularized Graphical Model Estimation in High Dimensions
Suprateek Kundu, Veera Baladandayuthapani, Bani K. Mallick
There has been an intense development of Bayes graphical model estimation approaches over the past decade - however, most of the existing methods are restricted to moderate dimensi…
Latent Factor Models for Density Estimation
Suprateek Kundu, David B. Dunson
Although discrete mixture modeling has formed the backbone of the literature on Bayesian density estimation, there are some well known disadvantages. We propose an alternative clas…
Bayes Variable Selection in Semiparametric Linear Models
Suprateek Kundu, David B. Dunson
There is a rich literature proposing methods and establishing asymptotic properties of Bayesian variable selection methods for parametric models, with a particular focus on the nor…