Qi Zheng

Tongji University

Jiading, Shanghai

👋 About Me

Hi! I am Qi Zheng, a Ph.D. student at Tongji University, expected to graduate in August 2026. My research focuses on efficient, scalable, and continually adaptive spatio-temporal forecasting, with a particular emphasis on time-series and graph-structured data. I mainly study complex dynamic modeling problems in sensor networks, including transportation, weather, and energy systems. My core interest is how to improve computational efficiency while maintaining strong predictive performance, and how to enable models to adapt to the evolving structures of both time and space.

My current research interests include spatio-temporal data mining, multivariate time-series analysis, temporal extrapolation, spatial adaptation, self-supervised representation learning, and spatio-temporal foundation models. I am currently exploring scalable modeling along both the temporal and spatial dimensions, and I hope to unify these two aspects to develop next-generation spatio-temporal foundation models for complex open-world environments.

✨ I am currently seeking opportunities in deep learning, machine learning, and foundation model (LLM) research or applications. 📬 If you are interested in related research, would like to discuss potential collaboration, or know of relevant opportunities, feel free to contact me!

Hi！我是郑启，同济大学博士研究生，预计于2026年8月毕业。我的研究聚焦于高效、可扩展且具备持续适应能力的时空预测方法，研究对象是时序与图结构数据，主要面向交通、气象、能源等传感网络中的复杂动态建模问题。我的核心关注点是，如何在保证预测性能的同时提升模型的计算效率，并使模型能够适应时间与空间结构的动态演化。

我当前的研究兴趣包括：时空数据挖掘、多变量时间序列分析、时间外推、空间自适应、自监督表征学习以及时空大模型。目前，我正在进一步研究时间维度和空间维度的可扩展建模问题，并计划将时间与空间两个维度的可扩展性结合起来，探索面向复杂开放环境的下一代时空基础模型。

✨ 我正在寻求深度学习/机器学习/大模型算法研究或应用方向的岗位。 📬 如果你对相关研究感兴趣，或希望交流合作、提供岗位，非常欢迎与我联系！

selected publications

[TITS’25] TLAST

TLAST: A Time-Lag Aware Spatial-Temporal Transformer for Traffic Flow Forecasting

Qi Zheng, Minhua Shao, and Yaying Zhang^*

IEEE Transactions on Intelligent Transportation Systems, 2025

DOI Bib HTML PDF

@article{TLAST,
  author = {Zheng, Qi and Shao, Minhua and Zhang, Yaying},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  title = {TLAST: A Time-Lag Aware Spatial-Temporal Transformer for Traffic Flow Forecasting},
  year = {2025},
  volume = {26},
  number = {9},
  pages = {13144-13158},
  doi = {10.1109/TITS.2025.3583391},
}

[AAAI’25] ST-ReP

ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

Qi Zheng, Zihao Yao, and Yaying Zhang^*

Proceedings of the AAAI Conference on Artificial Intelligence, 2025

DOI Bib HTML PDF

@article{STReP,
  title = {ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting},
  volume = {39},
  doi = {10.1609/aaai.v39i12.33465},
  number = {12},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  author = {Zheng, Qi and Yao, Zihao and Zhang, Yaying},
  year = {2025},
  pages = {13419-13427},
}

[DASFAA’23] TAGnn

TAGnn: Time Adjoint Graph Neural Network for Traffic Forecasting

Qi Zheng and Yaying Zhang^*

In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part I, 2023

DOI Bib HTML PDF

@inproceedings{TAGnn,
  author = {Zheng, Qi and Zhang, Yaying},
  title = {TAGnn: Time Adjoint Graph Neural Network for Traffic Forecasting},
  year = {2023},
  doi = {10.1007/978-3-031-30637-2_24},
  booktitle = {Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part I},
  pages = {369–379},
}

[TBD’22] DSTAGCN

DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting

Qi Zheng and Yaying Zhang^*

IEEE Transactions on Big Data, 2022

DOI Bib HTML PDF

@article{DSTAGCN,
  author = {Zheng, Qi and Zhang, Yaying},
  journal = {IEEE Transactions on Big Data},
  title = {DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting},
  year = {2022},
  volume = {9},
  number = {1},
  pages = {241-253},
  keywords = {Correlation;Roads;Forecasting;Convolution;Predictive models;Spectral analysis;Fuzzy neural networks},
  doi = {10.1109/TBDATA.2022.3156366},
}