Jie Feng is now a postdoctoral researcher at the Department of Electronic Engineering in Tsinghua University. Previously, he worked at Meituan as a researcher specializing in intelligent decision-making and large language models from 2021 to 2023. He received his B.S. and Ph.D. degrees (advised by Prof. Yong Li) in electrical engineering from Tsinghua University in 2016 and 2021, respectively. His research mainly focuses on large language models, spatiotemporal data mining and urban science, with over 30 papers published in top-tier venues including WWW, KDD, UbiComp, AAAI, TKDE, etc., attracting over 2400 citations. His research is supported by the Shuimu Tsinghua Scholar Program and the China Postdoctoral Talent Plan.

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. Feel free to contact me via email if you are interested.

Research Interests

• Large Language Models and Agents: investigating techniques for building domain-specific (Multi-Modal) LLMs and agents for urban systems
• Spatiotemporal Data Mining: trajectory mining, traffic forecasting
• Urban Science: human dynamics, urban dynamics

News

1. [New!] We release two preprint papers about LLM-based agent assisted mobility prediction: AgentMove and LIMP.
2. [New!] We release a preprint paper about enhancing the spatial cognition of large language models, CityGPT.
3. [New!] We release a preprint paper about benchmarking (multi-modal) large language models for urban tasks, CityBench.
4. [New!] Two papers are accepted in KDD 2024! High Efficiency Delivery Network and UniST.
5. [2023.12] We release a preprint paper about the Urban Generative Intelligence (UGI) in the era of large language models. For more details, please refer to arxiv.
6. [2023.04] Meituan’s Real-Time Intelligent Dispatching Algorithms wins the INFORMS 2023 Edelman Finalist Reward. I am glad to contribute to the part “Divide-and-Conquer” framework, the algorithm details about this is introduced in the High Efficiency Delivery Network.