Yutong Xie   谢雨桐

I am a Ph.D. candidate in the School of Information at the University of Michigan, where I work with Prof. Qiaozhu Mei as a member of the Foreseer Research Group. Prior to this, I received my Bachelor's degree from Shanghai Jiao Tong University as a member of the ACM Honors Class, where I was advised by Prof. Yong Yu and Prof. Weinan Zhang.

I have a general research interest in exploring the potential of AI to support and drive innovation, with a specific focus on its applications in both scientific research (AI for science) and creative processes (AI for creativity). My research encompasses three key aspects: identifying the innovation space ("What to innovate"), devising computational methodologies for innovative breakthroughs ("How to innovate"), and establishing robust criteria for evaluating these innovations ("How to evaluate"). A significant part of my work involves examining the synergistic relationship between AI and human intelligence in the innovation landscape. Additionally, I am keenly interested in the application and enhancement of large language models, particularly in the context of fostering effective human-AI collaboration for innovative endeavors.

  For more information, please check my curriculum vitae.
  yutxie AT umich DOT edu, yutongxie98 AT gmail DOT com
  Google Scholar, Semantic Scholar, Github, Twitter, LinkedIn

NEW! We made a video that shows the evolution of AI and data mining over half a century!
NEW! Our paper on text-to-image prompt analysis was accepted by WWW 2023 on the Creative Web Track!
NEW! Our paper on chemical space exploration measures was accepted by ICLR 2023!


For the full publication list, please refer to my Google Scholar profile.

A Prompt Log Analysis of Text-to-Image Generation Systems
Yutong Xie*, Zhaoying Pan*, Jinge Ma*, Luo Jie, Qiaozhu Mei.
The ACM Web Conference (WWW), 2023 (The Creative Web Track).
[PDF] [arXiv] [Code] [Video] [Slides]

How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules
Yutong Xie, Ziqiao Xu, Jiaqi Ma, Qiaozhu Mei.
International Conference on Learning Representations (ICLR), 2023.
[PDF] [Code] [SlidesLive] [Slides] [Poster] [AI Time Talk]

Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection
Ting Long*, Yutong Xie*, Xianyu Chen, Weinan Zhang, Qinxiang Cao, Yong Yu.
AAAI Conference on Artificial Intelligence (AAAI), 2022 (acceptance rate 15%).
[PDF] [Code] [SlidesLive] [Slides] [Poster]

MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, Lei Li.
International Conference on Learning Representations (ICLR), 2021.
Spotlight presentation (top 5%).
[PDF] [Code] [SlidesLive] [Slides] [Poster] [AI Time Talk] [WeChat Article]

Visual Rhythm Prediction with Feature-Aligned Network
Yutong Xie, Haiyang Wang, Zihao Xu, Yan Hao.
IAPR International Conference on Machine Vision Applications Conference (MVA), 2019.

* = equal contribution