Bingjun Li

I’m currently looking for full-time positions in 2025! Please contact me if you think I am a good candidate. Thank you!

Hi! My name is Bingjun Li. I am a fifth-year Ph.D. student in Computer Science at the School of Computing at the University of Connecticut under the supervision of Dr. Sheida Nabavi. I obtained my master’s in Statistics from the George Washington University in 2017 and my bachelor in Mathematics from Boston University in 2014.

My research interests are AI in Biomedical Science, multimodal analysis, and graph neural network. The work I do focuses on this three questions.

How to let the model learn the complex structure of biological process?
How to better fuse the multimodal data?
What can we do to make the model scale up to the future discovery and advancement in biology technology?

And here is the diagram of how all my publications fall into this three categories.

news

Oct 16, 2024	Our paper about Multimodal for Spatial Clustering is accepted to BIBM 2024!
Dec 20, 2023	Our paper about GNN framwork for cancer classification is accepted to BMC bioinformatics!
Oct 10, 2023	Our paper is accepted to BIBM 2023!
Aug 4, 2023	Our poster is accepted to ACM BCB 2023!
Nov 10, 2021	Our paper is accepted to BIBM!

selected publications

IEEE-BIBM

Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images

Bingjun Li, Mostafa Karami, Masum Shah Junayed, and 1 more author

arXiv preprint arXiv:2411.02534, 2024

Bib HTML

@article{li2024multi,
  title = {Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images},
  author = {Li, Bingjun and Karami, Mostafa and Junayed, Masum Shah and Nabavi, Sheida},
  journal = {arXiv preprint arXiv:2411.02534},
  year = {2024}
}

BMC-bioinfo

A multimodal graph neural network framework for cancer molecular subtype classification

Bingjun Li, and Sheida Nabavi

BMC bioinformatics, 2024

Bib HTML

@article{li2024multimodal,
  title = {A multimodal graph neural network framework for cancer molecular subtype classification},
  author = {Li, Bingjun and Nabavi, Sheida},
  journal = {BMC bioinformatics},
  volume = {25},
  number = {1},
  pages = {27},
  year = {2024},
  publisher = {Springer}
}

IEEE-BIBM

scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model

Bingjun Li, and Sheida Nabavi

In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023

Bib HTML

@inproceedings{li2023scgemoc,
  title = {scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model},
  author = {Li, Bingjun and Nabavi, Sheida},
  booktitle = {2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  pages = {2075--2080},
  year = {2023},
  organization = {IEEE}
}

ACM-BCB

Semi-supervised classification of disease prognosis using CR images with clinical data structured graph

Jun Bai, Bingjun Li, and Sheida Nabavi

2022

Bib HTML

@article{bai2022semi,
  title = {Semi-supervised classification of disease prognosis using CR images with clinical data structured graph},
  author = {Bai, Jun and Li, Bingjun and Nabavi, Sheida},
  booktitle = {Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
  pages = {1--9},
  year = {2022}
}

ACM-BCB

Cancer molecular subtype classification by graph convolutional networks on multi-omics data

Bingjun Li, Tianyu Wang, and Sheida Nabavi

2021

Bib HTML

@article{li2021cancer,
  title = {Cancer molecular subtype classification by graph convolutional networks on multi-omics data},
  author = {Li, Bingjun and Wang, Tianyu and Nabavi, Sheida},
  booktitle = {Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
  pages = {1--9},
  year = {2021}
}