Tang Qian

Xihu District, Hangzhou

Zhejiang University

Zetong Building

qt.tang.qian@zju.edu.cn

Biography

I am Tang Qian. I joined Ant Group as an Algorithm Engineer in April 2026. My current research focuses on pre-trained models for structured data and time series. I am also interested in vector search and distributed systems. My research at Zhejiang University’s School of Computer Science and Technology focused on efficient indexing and similarity search over large-scale data.

My work includes OneDB (SIGKDD 2026), QBase (TKDE 2026), and DIMS (TKDE).

For more details about my publications, experience, and achievements, please visit my CV page.

news

2026.10	Our paper QBase: A Query Aware Vector Search System has been accepted by IEEE TKDE 2026.
2026.09	Our preprint FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data has been updated on arXiv.
2026.08	Our paper OneDB: A Distributed Multi-Metric Data Similarity Search System has been published at SIGKDD 2026.
2026.04	I joined Ant Group as an Algorithm Engineer in April 2026. My research focuses on pre-trained models for structured data and time series.
2024.02	Our paper DIMS: Distributed Index for Similarity Search in Metric Spaces has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE 2024).
2022.07	Our paper Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting has been published at the International Joint Conference on Neural Networks (IJCNN 2022).

selected publications

FEAT

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data

Zhenghang Song, Tang Qian, Lu Chen, and 7 more authors

2026

Preprint

arXiv Bib HTML PDF

@misc{song2026feat,
  title = {FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data},
  author = {Song, Zhenghang and Qian, Tang and Chen, Lu and Li, Yushuai and Hu, Zhengke and Fang, Bingbing and Song, Yumeng and Zhao, Junbo and Zhang, Sheng and Li, Tianyi},
  year = {2026},
  eprint = {2603.16513},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  note = {Preprint},
}

OneDB

OneDB: A Distributed Multi-Metric Data Similarity Search System

Tang Qian, Yifan Zhu, Lu Chen, and 3 more authors

In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, 2026

DOI Bib HTML

@inproceedings{qian2026onedb,
  title = {OneDB: A Distributed Multi-Metric Data Similarity Search System},
  author = {Qian, Tang and Zhu, Yifan and Chen, Lu and Ke, Xiangyu and Li, Tianyi and Jensen, Christian},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  year = {2026},
  pages = {4024--4033},
  publisher = {ACM},
  doi = {10.1145/3770855.3817776},
}

DIMS

DIMS: Distributed Index for Similarity Search in Metric Spaces

Yifan Zhu, Chengyang Luo, Tang Qian, and 3 more authors

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024

Bib PDF

@article{zhu2024dims,
  title = {DIMS: Distributed Index for Similarity Search in Metric Spaces},
  author = {Zhu, Yifan and Luo, Chengyang and Qian, Tang and Chen, Lu and Gao, Yunjun and Zheng, Baihua},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2024},
  publisher = {IEEE},
}

QBase

QBase: A Query Aware Vector Search System

Boyu Tan, Tang Qian, Ziquan Fang, and 4 more authors

IEEE Transactions on Knowledge and Data Engineering (TKDE). Source Code can be found here , 2026

Bib PDF

@article{tan2023qbase,
  title = {QBase: A Query Aware Vector Search System},
  author = {Tan, Boyu and Qian, Tang and Fang, Ziquan and Chen, Lu and Wang, Mengzhao and Wang, Qilong and Zhao, Jingwen},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2026},
  url = {https://github.com/ZJU-DAILY/QBase},
}

ST-LGSL

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

Jiabin Tang, Tang Qian, Shijing Liu, and 3 more authors

2022

Bib PDF

@misc{tang2022spatiotemporallatentgraphstructure,
  title = {Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting},
  author = {Tang, Jiabin and Qian, Tang and Liu, Shijing and Du, Shengdong and Hu, Jie and Li, Tianrui},
  year = {2022},
  eprint = {2202.12586},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2202.12586},
  journal = {International Joint Conference on Neural Networks (IJCNN 2022)},
}