Jie Feng 冯杰

Jie Feng 冯杰

Postdoc in Tsinghua University

About Me

Jie Feng is currently a postdoctoral researcher in the Department of Electronic Engineering at Tsinghua University. From 2021 to 2023, he worked at Meituan as a researcher specializing in intelligent decision-making and large language models. He received his B.S. and Ph.D. degrees (advised by Prof. Yong Li) in electronic engineering from Tsinghua University in 2016 and 2021, respectively. His research mainly focuses on spatial intelligence, multi-modal large language models, human behavior modeling, urban science and spatiotemporal data mining, with over 40 papers published in top-tier venues including KDD, ACL, WWW, NAACL, AAAI, UbiComp, TKDE, etc. His work has garnered more than 3500 citations on Google Scholar. His research is supported by the Shuimu Tsinghua Scholar Program.

We are seeking self-motivated interns and collaborators to conduct research on spatio-temporal data mining, large language models, embodied agent, etc., with us remotely or at Tsinghua. Our interns have good record of publishing first-author papers in CCF-A conferences/journals. I also have strong connections with the industry (e.g., Meituan, Tencent) and can recommend good opportunities for internships or full-time positions. Feel free to contact me via email if you are interested.

Research Interests

  1. Large Language Models: investigating techniques for building domain-specific, multi-modal LLMs for urban systems, e.g., CityGPT and CityBench.
  2. LLM based Agents: building powerfull intelligent agents for urban applications, e.g., AgentMove, TrajAgent and LMP.
  3. Spatiotemporal Data Mining: trajectory mining, traffic forecasting, e.g., DeepMove, MoveSim, DeepSTN+, UniST.
  4. Urban Science: human dynamics, urban dynamics, in-depth and data-driven analyses of urban issues.

Survey

  1. Feng, Jie, Zeng Jinwei, et al. “A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science.” preprint 2025.04. Chinese article by Zhuanzhi.
  2. Xu, Fengli, Hao Qianyue, et al. “Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.” preprint 2025.01.
  3. Ding, Jingtao, Zhang Yunke, et al. “Understanding World or Predicting Future? A Comprehensive Survey of World Models.” preprint 2024.11.
  4. Xu, Fengli, Zhang Jun, Gao Chen, Feng Jie, Li Yong. “Urban Generative Intelligence (UGI): A foundational platform for agents in embodied city environment.” preprint 2023.12
  5. Li, Fuxian, Feng Jie, et al. “Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution.” ACM TKDD 2023

Talks

  1. “LLM-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science”, ACM SIGSPATIAL CHINA Saptial Data Intelligence 2025 Conference, Xiamen
  2. “LLM-Based Agentic Framework: A Novel Paradigm for Modeling Human Mobility”, ACM SIGSPATIAL CHINA Saptial Data Intelligence 2025 Conference, Xiamen
  3. “CityGPT: From LLM to Urban Generative Intelligence”, CCF ChinaData 2024 Conference, Boao

News

Selected Publications

* Equal contribution # Correspondence

Spatial Intelligence with Large Language Models

  1. CityGPT: Empowering Urban Spatial Cognition of Large Language Models
    Jie Feng*, Tianhui Liu*, Yuwei Du, Siqi Guo, Yuming Lin, Yong Li
    KDD 2025 Research Codes PDF
  2. CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
    Jie Feng*, Jun Zhang*, Tianhui Liu*, Xin Zhang, Tianjian Ouyang, Junbo Yan, Yuwei Du, Siqi Guo, Yong Li
    KDD 2025 D&B Codes PDF

Human Behavior Analytics and Modeling

  1. AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction
    Jie Feng, Yuwei Du, Jie Zhao, Yong Li
    NAACL 2025 Main Codes PDF
  2. Open-Set Living Need Prediction with Large Language Models
    Xiaochogn Lan, Jie Feng#, Yizhou Sun, Chen Gao, Jiahuan Lei, Xinleishi, Hengliang Luo, Yong Li#
    ACL 2025 Findings Codes PDF
  3. DeepMove: Predicting Human Mobility with Atentional Recurrent Networks
    Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin.
    WWW 2018 Codes(star 151) | PDF | Link | #5 Most Influential Paper in WWW 2018

AI-Driven Decision-Making

  1. Meituan’s Real-Time Intelligent Dispatching Algorithms Build the World’s Largest Minute-Level Delivery Network
    Yile Liang, Haocheng Luo, Haining Duan, Donghui Li, Hongsen Liao, Jie Feng, Jiuxia Zhao, Hao Ren, Xuetao Ding, Ying Cha, Qingte Zhou, Chenqi Situ, Jinghua Hao, Ke Xing, Feifan Yin, Renqing He, Yang Sun, Yueqiang Zheng, Yipeng Feng, Zhizhao Sun, Jingfang Chen, Jie Zheng, Ling Wang
    INFORMS 2023 Link | Edelman Finalist Reward | PDF
  2. Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
    Yile Liang, Jiuxia Zhao, Donghui Li, Jie Feng#, Chen Zhang, Xuetao Ding, Jinghua Hao, Renqing He
    KDD 2024 ADS PDF Link

Spatiotemporal Time Series Modeling

  1. UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
    Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li
    KDD 2024 Research Codes(star 180) | PDF | Link | #5 Most Influential Paper at KDD 2024
  2. DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
    Ziqian Lin*, Jie Feng*, Ziyang Lu, Yong Li, Depeng Jin
    AAAI 2019 Codes(star 66) PDF Link
  3. Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution
    Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, Yong Li
    TKDD 2023 Codes(star 296) | PDF | Link | #1 Most Cited Paper in TKDD 2023