Yu Zheng

I am currently a final-year PhD student at FIBLAB, Department of Electronic Engineering, Tsinghua University, advised by Prof. Depeng Jin and Prof. Yong Li. I received my bachelor degree from the Department of Electronic Engineering, Tsinghua University in 2019.

My research interests lie in the interdisciplinary area of artificial intelligence and urban science, and its applications to real-world complex systems. Most of my research is about developing innovative computational methods and intelligent decision-making tools to tackle long-standing challenges across offline urban spaces, physical systems and online information systems, including urban planning, network control, and information retrieval.

News

Sep 22, 2023	Our paper is selected for cover article in the latest issue of Nature Computational Science!
Sep 11, 2023	Our paper on automatic urban planning with reinforcement learning is published in Nature Computational Science!

Selected Publications

NatComputSci

Spatial planning of urban communities via deep reinforcement learning

Yu Zheng, Yuming Lin, Liang Zhao, Tinghai Wu, Depeng Jin, and Yong Li

Nature Computational Science, 2023

Bib HTML PDF Supp Code Poster

@article{zheng2023spatial,
  title = {Spatial planning of urban communities via deep reinforcement learning},
  author = {Zheng, Yu and Lin, Yuming and Zhao, Liang and Wu, Tinghai and Jin, Depeng and Li, Yong},
  journal = {Nature Computational Science},
  pages = {1--15},
  year = {2023},
  publisher = {Nature Publishing Group US New York},
  doi = {10.1038/s43588-023-00503-5},
  url = {https://www.nature.com/articles/s43588-023-00503-5},
}

KDD

Road Planning for Slums via Deep Reinforcement Learning

Yu Zheng*, Hongyuan Su*, Jingtao Ding, Depeng Jin, and Yong Li

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

arXiv Bib HTML PDF Code Poster

@inproceedings{zheng2023road,
  author = {Zheng*, Yu and Su*, Hongyuan and Ding, Jingtao and Jin, Depeng and Li, Yong},
  title = {Road Planning for Slums via Deep Reinforcement Learning},
  year = {2023},
  isbn = {9798400701030},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3580305.3599901},
  doi = {10.1145/3580305.3599901},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {5695–5706},
  numpages = {12},
  keywords = {slum upgrading, reinforcement learning, road planning},
  location = {Long Beach, CA, USA},
  series = {KDD '23},
}

WWW

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin

In Proceedings of the Web Conference 2021, 2021

arXiv Bib HTML PDF Code

@inproceedings{zheng2021disentangling,
  author = {Zheng, Yu and Gao, Chen and Li, Xiang and He, Xiangnan and Li, Yong and Jin, Depeng},
  title = {Disentangling User Interest and Conformity for Recommendation with Causal Embedding},
  year = {2021},
  isbn = {9781450383127},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3442381.3449788},
  doi = {10.1145/3442381.3449788},
  booktitle = {Proceedings of the Web Conference 2021},
  pages = {2980–2991},
  numpages = {12},
  keywords = {Recommender systems, causal embedding, popularity bias},
  location = {Ljubljana, Slovenia},
  series = {WWW '21},
}