Neuroimaging & Network Data

Graph Theory

Network Data

Imaging

Omics

Change-Point Detection

Interest Keywords

Graph Theory, fMRI, PET, Machine Learning, High-dimensional

Citation

BibTeX citation:

@online{untitled,
  author = {},
  title = {Neuroimaging \& {Network} {Data}},
  url = {https://kaizhongmu.github.io/research/graph_theory/},
  langid = {en}
}

For attribution, please cite this work as:

“Neuroimaging & Network Data.” n.d. https://kaizhongmu.github.io/research/graph_theory/.

---
title: "Neuroimaging & Network Data" 
categories: [Graph Theory, Network Data, Imaging, Omics, Change-Point Detection]
author: ""
date: ""
image: 1.png
citation: true
---  

## Interest Keywords

Graph Theory, fMRI, PET, Machine Learning, High-dimensional

## Related Experience

- **Research Project**: Non-Parametric Graph-Based Change-Point Detection Method for High-Dimensional Network Data
- **Research Project**: An Bayesian-based RNN Method for MNAR/MAR + left-censored longitudinal
- **Collaboration**: Translational Medical Research collaboration

## Related Story

I am deeply grateful to Dr. Hao Chen for introducing me to graph theory during my first research project, where it was applied to detect change points in social network data. This experience opened doors to numerous possibilities like high-dimensional data, omic data, neuroimaging. These are all areas I hope to explore further if the opportunity arises.