I am a final-year PhD candidate in Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), working with Prof. Le Song and Prof. Eric Xing on AI for Biology. I build biomolecular foundation models and use them for structure prediction, gene expression modeling, and multimodal learning.

I am also a research intern at GenBio AI, with prior roles at Carnegie Mellon University’s Sailing Lab and BioMap.

Research Interests

I use self-supervised and multimodal learning to link biological sequences to structure, expression, and function.

• Foundation models: Core contributor to 1.6B-parameter GB.RNA and 16B-parameter Mixture-of-Experts GB.Protein.
• Structure prediction: Improve RNA 3D structure prediction and protein complex modeling using language model representations.
• Gene expression: Predict tissue-specific RNA isoform expression by integrating DNA, RNA, and protein data, and curate a GTEx-derived dataset.
• Model adaptation: Develop sequence-level tasks and multimodal fusion in GB.ModelGenerator, and evaluate RNA and protein models across 200+ tasks.

I’m passionate about building the next generation of RNA foundation models to advance mRNA therapeutics. I welcome collaborations!

Research Experience

• Research Intern, GenBio AI, Abu Dhabi, UAE. Dec 2024–Dec 2025; Jun 2026–present.
• Visiting PhD Student, Sailing Lab, Carnegie Mellon University, Pittsburgh, USA. Jun–Sep 2024. Host: Prof. Eric Xing.
• Algorithm Intern, BioMap, Beijing, China. Jul–Sep 2023.
• Research Assistant, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Jul 2019–May 2022.

Publications

• Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, and Eric P. Xing. A large-scale foundation model for RNA function and structure prediction. Under review at Nature Methods. NeurIPS 2024 AIDrugX Workshop (Spotlight). [Paper] [GitHub] [Hugging Face]
• Ning Sun, Shuxian Zou, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, and Eric P. Xing. Mixture of experts enable efficient and effective protein understanding and design. Under review at Nature Methods. NeurIPS 2024 AIDrugX Workshop (Spotlight). [Paper] [GitHub] [Hugging Face]
• Shuxian Zou, Jiayou Zhang, Bingkang Zhao, Hui Li, Eric P. Xing, and Le Song. Improving RNA 3D Structure Prediction via Language Model-Augmented AlphaFold 3. NeurIPS 2025 MLSB Workshop. [Paper]
• Youhan Lee, Shujun He, Toshiyuki Oda, …, Shuxian Zou, …, David Baker, …, Rhiju Das. Template-based RNA structure prediction advanced through a blind code competition. Accepted in principle at Nature Methods. [Paper]
• Caleb Ellington, Dian Li, Shuxian Zou, Elijah Cole, Ning Sun, Sohan Addagudi, Le Song, and Eric P. Xing. Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator. ICML 2025 GenBio Workshop (Spotlight). [Paper] [GitHub]
• Shuxian Zou, Hui Li, Shentong Mo, Xingyi Cheng, Eric P. Xing, and Le Song. Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction. arXiv preprint, 2023. [Paper]
• Shuxian Zou, Shaonan Wang, Jiajun Zhang, and Chengqing Zong. Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding. Findings of ACL 2022, pp. 648–657. [Paper] [GitHub]
• Shuxian Zou, Shaonan Wang, Jiajun Zhang, and Chengqing Zong. Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models. NeurIPS 2021 AI for Science Workshop, Dec 13, 2021. [Paper]

Competitions

• Stanford RNA 3D Folding Kaggle Competition (2025) — Gold Medal, 6th/1,516 teams.
  • Team: Littletree🎄 & Moth & Bianco.
  • Contributors: Shuxian Zou, Alejo Paullier, Bingkang Zhao.
  • Contribution: augmented Protenix with AIDO.RNA embeddings for RNA 3D prediction.
  • Solution write-up
• 3rd Magic Mirror Cup: Customer Service Question Similarity (2018) - 16th/359 teams (first round); 12th/95 (final).
  • Team: moka_tree.
  • Contributors: Lei Zhu, Shuxian Zou.
  • Solution write-up (Chinese) · GitHub