Pengfei Gu 谷鹏飞

Assistant Professor

Department of Computer Science
University of Texas Rio Grande Valley
1201 West University Dr.
Edinburg, TX 78539-2999, USA

Office: EIEAB 3.236
Email: pengfei.gu01 at utrgv dot edu

Biography

I am currently an Assistant Professor at the University of Texas Rio Grande Valley in the Computer Science department. I obtained my Ph.D. from the University of Notre Dame, under the supervision of Dr. Danny Z. Chen and Dr. Chaoli Wang. Previously, I received an M.S. in Computer Science and an M.S. in Mathematics from the University of Texas Rio Grande Valley and a bachelor's degree in Mathematics from Tianjin University of Technology and Education.

Research Interests

Deep learning for medical image analysis (e.g., image segmentation, classification, and registration)

Topology-driven image analysis
Self-supervised learning
Large foundation models/Large language models in medical imaging
Multi-modal data analysis

Deep learning for scientific visualization

Scientific data generation
Scientific data compression

Openings: I am looking for new full-sponsered PhD students to join my research team. If you are passionate about related research topics, please email me with your CV and a few sentences about your research experience and interests.

News

[08/2024] Joined CS Department at UTRGV as a tenure-track Assistant Professor.
[06/2024] One paper was accepted by PRCV 2024.
[06/2024] One paper was accepted by Bioinformatics Advances.
[06/2024] One paper was accepted by MICCAI 2024.
[06/2024] Our paper on Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders is available on arXiv.
[06/2024] One paper was accepted by IEEE VIS Short Papers 2024.
[05/07/2024] Defended the PhD dissertation and became a Dr.!
[02/2024] Two papers were accepted by ISBI 2024.
[12/2023] One paper was accepted by Medical Image Analysis.
[08/2023] One paper was accepted by C&G.
[06/2023] One paper was accepted by MICCAI 2023.
[01/2023] Three papers were accepted by ISBI 2023.
[10/2022] One paper was accepted by BIBM 2022.

Teaching

Fall 2024

CS3310: Mathmatical Foundation of CS

Selected Publications [Google Scholar]

	FCNR: Fast Compressive Neural Representation of Visualization Images. Yunfei Lu, Pengfei Gu, and Chaoli Wang. In Proceedings of IEEE VIS Short Papers, 2024. [paper][code]
	IHCSurv: Effective Immunohistochemistry Priors for Cancer Survival Analysis in Gigapixel Multi-stain Whole Slide Images. Yejia Zhang, Hanqing Chao, Zhongwei Qiu, Wenbin Liu, Yixuan Shen, Nischal Sapkota, Pengfei Gu, Danny Z. Chen, Yun Bian, Hui Jiang, Le Lu, Ke Yan, and Dakai Jin. In Proceedings of International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2024. [paper][code]
	Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation. Pengfei Gu, Yejia Zhang, Huimin Li, Chaoli Wang, and Danny Z. Chen. Preprint, 2024. [paper]
	Boosting Medical Image Classification with Segmentation Foundation Model. Pengfei Gu, Zihan Zhao, Hongxiao Wang, Yaopeng Peng, Yizhe Zhang, Nishchal Sapkota, Chaoli Wang, and Danny Z. Chen. In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 2024. [Oral Presentation] [paper]
	NeRVI: Compressive Neural Representation of Visualization Images for Communicating Volume Visualization Results. Pengfei Gu, Danny Z. Chen, and Chaoli Wang. Computers & Graphics (C&G), 2023. [paper][code]
	SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings. Yejia Zhang, Pengfei Gu, Nishchal Sapkota, and Danny Z. Chen. In Proceedings of International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2023. [paper][code]
	A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning. Yejia Zhang, Pengfei Gu, Nishchal Sapkota, Hao Zheng, Peixian Liang, and Danny Z. Chen. (* means equal contribution) In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 2023. [Oral Presentation] [paper]
	ConvFormer: Combining CNN and Transformer for Medical Image Segmentation. Pengfei Gu, Yejia Zhang, Chaoli Wang, and Danny Z. Chen. (* means equal contribution) In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 2023. [Oral Presentation] [paper]
	GrNT: Gate-regularized network training for improving multi-scale fusion in medical image segmentation. Yizhe Zhang, Pengfei Gu, Yejia Zhang, Chaoli Wang, and Danny Z. Chen. (* means equal contribution) In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 2023. [Oral Presentation] [paper]
	Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images. Yejia Zhang, Nishchal Sapkota, Pengfei Gu, Yaopeng Peng, Hao Zheng, and Danny Z. Chen. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022. [paper]
	Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning. Pengfei Gu, Jun Han, Danny Z. Chen, and Chaoli Wang. In Proceedings of IEEE Pacific Visualization Symposium (IEEE PacificVis), 2022. [paper][code]
	kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation. Pengfei Gu, Hao Zheng, Yizhe Zhang, Chaoli Wang, and Danny Z. Chen. In Proceedings of International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2021. [paper]
	Reconstructing Unsteady Flow Data from Representative Streamlines via Diffusion and Deep Learning Based Denoising. Pengfei Gu, Jun Han, Danny Z. Chen, and Chaoli Wang. IEEE Computer Graphics and Applications (IEEE CG&A), 2021. [IEEE CG&A 2021 Best Paper Award] [paper][code]
	Approximate set union via approximate randomization. Bin Fu, Pengfei Gu , and Yuming Zhao. ( means corresponding author) Theoretical Computer Science (TCS), 2021. [paper]