Junqi Wang, PhD

Junqi Wang

Post-doctoral Research Fellows

Junqi Wang, PhD, is a research fellow in the lab. He received his BS degree in Math and Applied Mathematics from Nanjing University of Science and Technology (Nanjing, China). He earned his Ph.D. degree in Biomedical Engineering in 2021 from Tulane University (New Orleans, LA, US). His research focuses on developing graph-based approaches for medical image analysis. Specifically, he applied graph topological features, graph signal processing, and graph deep learning models to study mental disorders (schizophrenia, ADHD, bipolar disorder) and brain development. His current studies include disease prediction and biomarker detection with hypergraph learning based deep learning models.

Biography & Affiliation

Research Interests

Machine learning; graph signal processing; deep learning; medical imaging analysis

Academic Affiliation

Post-doctoral Research Fellows


Perinatal Institute


Post-doc Research Fellow

Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 2021.

Doctor of Philosophy (Ph.D.)

Biomedical Engineering, Tulane University, New Orleans, LA, 2021

Bachelor of Science (B.S)

Math and Applied Mathematics, Nanjing University of Science and Technology, China 2015

  • G. Qu, L. Xiao, W. Hu, J. Wang, K. Zhang, V. Calhoun, YP. Wang. Ensemble manifold regular-ized multi-modal graph convolutional network for cognitive ability prediction. IEEE Transactions on Biomedical Engineering 2021.
  • J. Wang, L. Xiao, W. Hu, G. Qu, V. Calhoun, J. Stephen, T. Wilson, YP. Wang.. Functional Network Estimation Using Multi-graph Learning with Application to Brain Maturation Study. Human Brain Mapping, 2021 42.9 (2021): 2880-2892.
  • J. Wang, V. Calhoun, J. Stephen, T. Wilson, YP. Wang. Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI. SPIE medical imaging 2020.
  • L. Xiao, J. Wang, V. Calhoun, J. Stephen, T. Wilson, YP. Wang. Multi-Hypergraph Learning Based Brain Functional Connectivity Analysis in fMRI Data. IEEE Transactions on Medical Imaging (2019).
  • J. Wang, V. Calhoun, J. Stephen, T. Wilson, YP. Wang. Integration of network topological features and graph Fourier transform for fMRI data analysis. ISBI, IEEE 2018 (pp. 92-96).
  • J. Wang, L. Xiao, V. Calhoun, J. Stephen, T. Wilson, YP. Wang.. Examining Brain Maturation during Adolescence Using Graph Laplacian Learning Based Fourier Transform. Journal of Neuroscience Methods 2020, 108649.