CHANGGEE CHANG

Assistant Professor

Indiana University School of Medicine

United States

Research Interests

Explore related searches

Professors in United States Professors in Statistics Statistics professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Changseng Chang is an Assistant Professor at Indiana University School of Medicine in the United States. His research primarily focuses on brain imaging genomics, cancer survival dynamics, and advanced statistical models for health data analysis. Recent articles have explored topics such as genetic influences on brain structure, Bayesian modeling techniques, and diagnosing kidney obstruction using integrative approaches.

Articles (10)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2020

Year:

2020

Collaborators (5)

Jeong Hoon Jang

Assistant Professor

Yonsei University

SOUTH KOREA

View details

Junhao WEN

Assistant Professor

University of Southern California

UNITED STATES

View details

Li Shen

Professor of Informatics

University of Pennsylvania

UNITED STATES

View details

Qi Long

Professor

University of Pennsylvania

UNITED STATES

View details

Jia Xin Yu

Institute of Cancer Research

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

CHANGGEE CHANG

Research Interests

Explore related searches

Contact this professor

About

Articles (10)

A genetically informed brain atlas for enhancing brain imaging genomics

Circulating KRAS G12D but not G12V is associated with survival in metastatic pancreatic ductal adenocarcinoma

Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors

CEDAR: communication efficient distributed analysis for regressions

An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction

Integrative learning of structured high‐dimensional data from multiple datasets

Genetic Underpinnings of Brain Structural Connectome for Young Adults

Multiple imputation for analysis of incomplete data in distributed health data networks

A Bayesian Latent Class Model to Predict Kidney Obstruction in the Absence of Gold Standard

Collaborators (5)

Jeong Hoon Jang

Junhao WEN

Li Shen

Qi Long

Jia Xin Yu

Make your next move.