Qi Zheng

Tongji University

Jiading, Shanghai

👋 About Me

Hi, I’m Qi Zheng (郑启). I am a Ph.D. candidate at Tongji University (expected Aug 2026) specializing in efficient and scalable spatial-temporal forecasting for real-world sensor networks (traffic, climate, and energy).
My research aims to make forecasting models both high-performing and adaptable-efficient in computation and flexible in handling evolving temporal and spatial structures.

My research interests include spatio-temporal data mining, multivariate time-series analysis, self-supervised representation learning, and foundation models (LLMs). Currently, I am extending this line toward temporal scalability and ultimately integrating both time and space scalability into future large spatial-temporal foundation models.

✨ I’m seeking roles in deep learning/machine learning, spatial-temporal modeling, or foundation-model (LLM) algorithm research and applications. 📬 If you’re interested in my work, open to collaboration, or have relevant opportunities, I’d be glad to connect!

Hi！我是郑启，同济大学博士研究生（预计于2026年8月毕业），研究方向为高效且可扩展的时空预测建模，主要面向交通、气象与能源等传感网络。我的研究目标是让预测模型既具备高性能与高适应性，又在计算上高效、能灵活应对时间与空间结构的动态变化。

我的研究兴趣包括：时空数据挖掘（spatial-temporal data mining）、多变量时间序列分析（multivariate time-series analysis）、时间外推（temporal extrapolation）、空间自适应（spatial adaptation）、自监督表征学习（representation transfer）以及大模型（foundation models, LLMs）。目前，我正在进一步探索时间维度的可扩展性（temporal scalability），并计划在未来将时间与空间两方面的可扩展性相结合，构建下一代时空大模型（spatial-temporal foundation models）。

✨ 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},
}