38 citations · 75 across the 20 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
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…