Cornell University Artificial Intelligence

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Cornell University Artificial Intelligence (CUAI) is an undergraduate student organization that focuses on machine learning research and publication. Each year, club members work closely with club officers to aim to publish research papers at top ML conferences like NeurIPS, ICML, etc. We welcome students from all backgrounds and experience levels who are passionate about AI/ML. The club meets weekly to discuss recent ML papers and advance our own research projects. Our goal is to foster an inclusive, collaborative environment where students can gain hands-on research experience and connect with others with similar interests.


CUAI is recruiting for Fall 2023! We encourage undergraduates from all disciplines to apply, and will also be offering a limited number of coffee chats on a first come, first served basis. Please follow this link here to apply, and this link here to indicate interest in a coffee chat and/or information session.

Note: you may choose to fill out either or both forms, but only submissions to the application form will be considered for admission.

We will be holding an information session in on Saturday, August 26th, from 1-3 pm in Upson 142!

Recent Publications

Test-Time Distribution Normalization for Contrastively Learned Visual-language Models

NeurIPS 2023.

Yifei Zhou*, Juntao Ren*, Fengyu Li*, Ramin Zabih, Ser-Nam Lim

Riemannian Residual Neural Networks

NeurIPS 2023.

Isay Katsman*, Eric Chen*, Sidhanth Holalkere*, Anna Asch*, Aaron Lou, Ser-Nam Lim, Christopher De Sa

\(BT^2\): Backward-compatible Training with Basis Transformation

ICCV 2023.

Yifei Zhou*, Zilu Li*, Abhinav Shrivastava, Hengshuang Zhao, Antonio Torralba, Taipeng Tian, Ser-Nam Lim

Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation

ICCV 2023.

Eric Chen, Sidhanth Holalkere, Ruyu Yan, Kai Zhang, Abe Davis

GAPX: Generalized Autoregressive Paraphrase-identification X

NeurIPS 2022.

Yifei Zhou, Renyu Li, Hayden Housen, Ser-Nam Lim

Equivariant Manifold Flows

NeurIPS 2021.

Isay Katsman*, Aaron Lou*, Derek Lim*, Qingxuan Jiang*, Ser-Nam Lim, Christopher De Sa

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

NeurIPS 2021.

Derek Lim*, Felix Hohne*, Xiuyu Li*, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, Ser-Nam Lim

Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

ICLR 2021.

Qian Huang*, Horace He*, Abhay Singh, Ser-Nam Lim, Austin Benson

Neural Manifold Ordinary Differential Equations

NeurIPS 2020.

Aaron Lou*, Derek Lim*, Isay Katsman*, Leo Huang*, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

Better Set Representations For Relational Reasoning

NeurIPS 2020.

Qian Huang*, Horace He*, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin Benson

Differentiating through the Fr├ęchet Mean

ICML 2020.

Aaron Lou*, Isay Katsman*, Qingxuan Jiang*, Serge Belongie, Ser-Nam Lim, Christopher De Sa

Enhancing Adversarial Example Transferability with an Intermediate Level Attack

ICCV 2019.

Qian Huang*, Isay Katsman*, Horace He*, Zeqi Gu*, Serge Belongie, Ser-Nam Lim

Adversarial Example Decomposition

ICML 2019 Workshop, Security and Privacy of Machine Learning.

Horace He, Aaron Lou*, Qingxuan Jiang*, Isay Katsman*, Serge Belongie, Ser-Nam Lim

* indicates equal contribution


Each year, a tight nit group of undergraduate students get together to push out innovative machine learning research, spanning multiple disciplines. We maintain a close relation with graduate and faculty advisors who oversee our work. Many of our alumni go on to accomplish great things across many disciplines upon graduating.

  • Current Members
  • Research Advisors
  • Alumni


Sponsored by and in Direct Collaboration with Facebook AI