Yi Zhou (周奕)

About Me
I'm currently employed as a research scientist at ByteDance Research, intensively focusing on AI for science and Artificial Genreral Intelligence. Natural language processing (NLP) is my "native language".

I established the cryo-EM research team at Bytedance Research, and our first work CryoSTAR was accepted by Nature Methods. I believe that our effort to empowering AI for biology is well recognized by biologists.

Besides that, I have a fair understanding and experience in deep generative modeling (protein design, 3d generation, etc.), traditional NLP tasks (labeling, parsing, translation, etc.), and of course, the popular LLM.

I was part of the Fudan NLP group and earned my Master's degree from Fudan University (2017~2021), under the mentorship of Prof. Xiaoqing Zheng. In 2020, I gained invaluable experience visiting the University of California, Los Angeles. During this visit, I had the opportunity to collaborate with Prof. Cho-Jui Hsieh and Prof. Kaiwei Chang, focusing primarily on the topic of model security in the field of NLP. Since 2021, I joined the ByteDance AI Lab, where I have been working closely with Prof. Hao Zhou(2021~2022) and Prof. Quanquan Gu(2023~).

Email: zhouyi.naive[-]bytedance[-]com / dugu9sword[-]gmail[-]com / yizhou17[-]fudan[-]edu[-]cn

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Research Interests
(A full list can be found on Google Scholar.)
AI for Science: Cryo-EM
  • CryoSTAR: Leveraging Structural Prior and Constraints for Cryo-EM Heterogeneous Reconstruction (Nature Methods)
  • CryoFM: A Flow-based Foundation Model for Cryo-EM Densities (ICLR 2025)
  • AI for Science: Drug Discovery
  • ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design (arxiv)
  • Structure-informed Language Models Are Protein Designers (ICML 2023, oral)
  • Regularized Molecular Conformation Fields (NeurIPS 2022, spotlight)
  • Zero-Shot 3D Drug Design by Sketching and Generating (NeurIPS 2022, spotlight)
  • NLP
  • The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21 (EMNLP-WMT 2021)
  • Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble (ACL 2021)
  • Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (ACL 2020)
  • Softwares
  • cryostar, a neural network based framework for recovering conformational heterogenity of protein complexes.
  • torch-random-fields, a library for building markov random fields (MRF) with complex topology with pytorch, it is optimized for batch training on GPU.
  • manytasks, a lightweight tool for deploying many tasks automatically, without any modification to your code.
  • paragen, a PyTorch deep learning framework for parallel sequence generation and beyond.
  • Academic Services
  • Reviewer of NLP conferences: COLM 2024/2025, ACL 2023, EMNLP 2022/2023, COLING 2024/2025
  • Reviewer of ML conferences: NeurIPS 2022/2023/2024/2025, ICML 2023/2024/2025, ICLR 2024/2025, ICLR GEM 2024/2025, IJCAI 2023/2024, AAAI 2025
  • Reviewer of AI journals: JMLR, AI


  • Updated at Sep. 2022
    Thanks Jon Barron for this amazing work