About me

I am a third-year Ph.D. candidate in the Department of Computer Science and Engineering at The University of Texas at Arlington, supervised by Prof. Junzhou Huang.
Before that, I obtained a Bachelor of Applied Bioscience from Chu Kochen Honors College at Zhejiang University.

My research interests lie broadly in the following areas:

• Multi-modal Learning, e.g., Multi-modal LLM, Multi-modal Representation Learning, Multi-modal Fusion
• Multi-instance Learning
• Their applications in computational pathology

You can find my CV here: Qifeng Zhou’s CV

🚀 I am actively looking for Summer 2026 internship and academic collaboration opportunities. Feel free to reach out via email!

News

• 2025.06: Two papers are accepted by MICCAI 2025! 🎉
• 2025.06: Start Research Intern in Genmab! 🎉
• 2025.01: One paper is accepted by ISBI 2025! 🎉
• 2024.09: One paper is accepted by ACM BCB 2024! 🎉
• 2024.05: One paper is accepted by MICCAI 2024 (early accepted)! 🎉

Selected Publications

1. Qifeng Zhou, Thao M Dang, Yuzhi Guo, Hehuan Ma, Wenliang Zhong, Saiyang Na, Jean Gao and Junzhou Huang, “Contrastive Pretraining for Computational Pathology With Visual Language Models”, ISBI 2025.

2. Qifeng Zhou, Wenliang Zhong, Yuzhi Guo, Michael Xiao, Hehuan Ma, and Junzhou Huang,
   “PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning”,
   MICCAI 2024.

3. Thao Dang, Haiqing Li, Yuzhi Guo, Hehuan Ma, Feng Jiang, Yuwei Miao, Qifeng Zhou, Jean Gao, and Junzhou Huang, “HAGE: Hierarchical Alignment Gene-Enhanced Pathology Representation Learning with Spatial Transcriptomics”, MICCAI 2025.

4. Thao M. Dang, Yuzhi Guo, Hehuan Ma, Qifeng Zhou, Saiyang Na, Jean Gao, and Junzhou Huang,
   “MFMF: Multiple Foundation Model Fusion Networks for Whole Slide Image Classification”,
   ACM BCB 2024.

Experience

• Digital Pathology Intern @ Genmab Princeton, NJ, USA Jun. 2025 - Aug. 2025
  • Engineered an end-to-end multi-modal deep learning framework using cross-attention to fuse Whole Slide Images (WSI), omics, and clinical data for patient survival analysis in oncology.
  • Developed a novel deep learning pipeline for non-invasive prediction of Folate Receptor Alpha (FRα) expression from H&E pathology images, creating a scalable method for computational biomarker discovery.
  • Managed model development and large-scale data processing in a GPU-enabled cloud environment (AWS, Databricks) utilizing Python and PyTorch.

Education

• 2022.09 – Present: Ph.D. Student, The University of Texas at Arlington, Arlington, TX, USA
• 2018.09 – 2022.06: Undergraduate, Chu Kochen Honors College, Zhejiang University, Hangzhou, China

Honors and Awards

• Zhejiang University First-class Scholarships