Curriculum vitae

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Research profile

I am a joint Ph.D. student in Computer Science at Zhejiang University and Westlake University, developing generative models for functional protein design. My work combines large-scale pretraining and multimodal learning to connect natural-language instructions with protein sequences.

Research interests: Protein language models, protein design, generative AI, AI for science, and RLHF.

Education

  • Zhejiang University & Westlake University · Sep. 2023 – Jul. 2028 (expected)
    Joint Ph.D. student in Computer Science.
  • Shanghai University · Sep. 2019 – Jul. 2023
    Bachelor’s degree in Computer Science and Technology.

Selected publications

* Equal contribution. Selected publications are listed below; see Google Scholar for more.

Programmable design of functional proteins from natural language.

Fengyuan Dai*, Shiyang You*, et al.

Nature, 2026

Pinal is a 16-billion-parameter generative model pretrained on 1.7 billion protein–text pairs for designing proteins from natural language. Experiments confirmed the intended functions across four design tasks, including fluorescence and enzyme catalysis, without iterative experimental optimization.

Decoding the Molecular Language of Proteins with Evolla.

Xibin Zhou*, Chenchen Han*, …, Fengyuan Dai, et al.

bioRxiv, 2025

Evolla is an interactive protein-language model trained on 546 million protein–text pairs and refined through direct preference optimization. It supports natural-language queries about protein function, with zero-shot performance comparable to state-of-the-art supervised prediction.

Awards

  • National Scholarship, 2026.
  • First Class Academic Scholarship, 2022.
  • Second Class Academic Scholarship, 2020 and 2021.

Technical skills

  • Programming and tools: Python, PyTorch, LaTeX, Linux, Git.
  • Languages: Chinese (native), English (fluent).

Contact

Hangzhou, Zhejiang, China · da1cantst0p@gmail.com