Yining (Annie) Tang

Annie joined us in October 2025 to use AI-based approaches to study how coding sequences vary across the tree of life and how this can inform mRNA design for heterologous protein expression.

Biography

Annie is a PhD student in the Bornelöv Lab at the Department of Biochemistry, University of Cambridge. She holds an M.S. in Applied Computing from the University of Toronto and a B.S. in Statistics from the University of California, Davis. Her research interest focuses on leveraging artificial intelligence to address key challenges in the biological sciences at the molecular level, with the aspiration of pushing the boundary of scientific understanding. Specifically, she has worked with large language models for multi-omics analysis and deep generative models for protein modeling.

Research positions

PhD Student (Oct 2025 - now)
Department of Biochemistry, University of Cambridge, UK

Qualifications

• MS in Applied Computing, University of Toronto, Canada, Dec 2024
• BS in Statistics, University of California Davis, USA, Dec 2020

Honours and awards

• Statistical Data Science Award Scholarship, University of Toronto (Jun 2024)
• Dean's Honor List, University of California Davis (Fall 2019, Winter 2019, Winter 2020, Spring 2020)

Key publications

• Haonan He*, Yuchen Ren*, Yining Tang*, Ziyang Xu*, Junxian Li, Minghao Yang, Di Zhang, Dong Yuan, Tao Chen, Yuqiang Li, Shufei Zhang, Dongzhan Zhou, Wanli Ouyang, Peng Ye. Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models. The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Kaihui Cheng*, Ce Liu*, Qingkun Su, Jun Wang, Liwei Zhang, Yining Tang, Yao Yao, Siyu Zhu, Yuan Qi. AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance. The 39th Association for the Advancement of Artificial Intelligence (AAAI), 2025.