Jing Nathan Yan

I am a Ph.D. student at Bowers CIS, Cornell/Cornell Tech where I am extremely fortunate to be advised by Prof. Sasha Rush and Prof. Jeff Rzeszotarski. Before coming to Cornell, I have been affiliated to HKU (courses) working with Prof. Reynold Cheng and SFU working with Prof. Jiannan Wang.

I had the great fortunate to spend summers working with fantastics researchers at Google Research, Microsoft Research and Facebook AI Labs.

My research interest is in Large-scale Deep Learning Systems with the focus on making them more efficient and robust.

Currently I am fascinated with Alternative Architectures for large-scale systems on Long Sequence Modeling (i.e., long text window, image, 3D object and video generation). I also work on mitigating Societal Bias in machine learning systems.

Here is my Email: jy858 AT cornell.edu

Nathan Yan

Selected Recent Publications[Full List]

  1. Jing Nathan Yan*, Jiatao Gu*, Alexander Rush
    Diffusion Models Without Attention.
    In CVPR 2024, Jun 17 - 21, Seattle, United States. (*equal contribution)
    A shorter version at NeurIPS 2023 Workshop on Diffusion Models
  2. Jing Nathan Yan, Tianqi Liu, Justin T. Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Yao Zhao, Charu Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
    Predicting Text Preference Via Structured Self-Comparative Reasoning.
    In ACL 2024, August 11 – 16, Bangkok, Thailand. [arXiv link]
  3. Yi Lu*, Jing Nathan Yan*, Songlin Yang, Justin Chiu, Siyu Ren, Fei Yuan, Wenting Zhao, Zhiyong Wu, Alexander Rush
    A Controlled Study on Long-Context Extension and Generalization in LLMs.
    Preprint 2024. (*equal contribution)
  4. Qi Sun, Zhiyang Guo, Ziyu Wan, Jing Nathan Yan, Shengming Yin, Wengang Zhou, Jing Liao, Houqiang Li
    EG4D: Explicit Generation of 4D Object without Score Distillation.
    Preprint 2024.
  5. Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
    Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning.
    In ACL 2024, August 11 – 16, Bangkok, Thailand. [arXiv link]
  6. Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, Alexander Rush
    MambaByte: Token-free Selective State Space Model.
    Preprint 2024. [arXiv link]
  7. Junxiong Wang, Jing Nathan Yan, Albert Gu, Alexander Rush
    Pretraining Without Attention.
    In EMNLP 2023 Findings, Dec 5 - 10, Singapore.
  8. Jing Nathan Yan, Ziwei Gu, Jeff Rzeszotarski
    Tessera: Discretizing Data Analysis Workflows on a Task Level.
    In CHI 2021, May 8 - 13, Yokohama, Japan.
  9. Ziwei Gu*, Jing Nathan Yan*, Jeff Rzeszotarski
    Understanding User Sensemaking in Machine Learning Fairness Assessment Systems.
    In TheWeb 2021(WWW 2021), April 19 - 23, Ljubljana, Slovenia(*equal contribution). [Datasets]
  10. Jing Nathan Yan, Oliver Schulte, Mohan Zhang, Jiannan Wang, Reynold Cheng
    SCODED: Statistical Constraint Oriented Data Error Detection.
    In SIGMOD 2020, June 14 - 19, Portland, United States.
  11. Jing Nathan Yan, Ziwei Gu, Hubert Lin, Jeff Rzeszotarski
    Silva: Interactively Assessing Machine Learning Fairness Using Causality.
    In CHI 2020, April 25 - 30, Hawai'i, United States.


Professional Activities

Program Committee

  • FaccT 2023





    Reading books(The GAY GENIUS SU SHI), Doing random Weight lifting, and Trying to practice golfing more. Also a big fan of many Japanese Cartoons 名探偵コナン and 夏目友人帳.

    Was an active member in Foreign Affairs Association. Co-founder of PowerFair, a NGO platform based in Hong Kong which aims at promoting the equal access to public resources (i.e., data).

    Previous band member, good at making noises.



  Adapted from a template by Prof. Jiannan Wang