Tang Qian

杭州西湖区

浙江大学玉泉校区

则通楼

qt.tang.qian@zju.edu.cn

个人简介

我是钱唐，自 2026 年 4 月起在蚂蚁集团担任算法工程师。当前研究方向为预训练结构化数据模型与预训练时序模型，同时关注向量检索与分布式系统。曾在浙江大学计算机科学与技术学院开展大规模数据索引与相似性检索研究。

相关研究包括 OneDB（SIGKDD 2026）、QBase（TKDE 2026）和 DIMS（TKDE）。

关于我的论文、经历和奖项等更多详细信息，请访问我的个人简历页面。

news

2026.10	论文 QBase: A Query Aware Vector Search System 被 IEEE TKDE 2026 接收。
2026.09	预印本 FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data 已在 arXiv 更新。
2026.08	论文 OneDB: A Distributed Multi-Metric Data Similarity Search System 发表于 SIGKDD 2026。
2026.04	2026 年 4 月加入蚂蚁集团，担任算法工程师。研究方向：预训练结构化数据模型与预训练时序模型。
2024.02	论文 DIMS: Distributed Index for Similarity Search in Metric Spaces 被 IEEE Transactions on Knowledge and Data Engineering（TKDE 2024）接收。
2022.07	论文 Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting 发表于 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)},
}