Kaizhen Tan

Kaizhen Tan

Focusing on Urban AI and Spatial Intelligence

Hi! I’m Kaizhen Tan (谭楷蓁). I am currently a master’s student in Artificial Intelligence at Carnegie Mellon University. I received my bachelor’s degree in Information Systems from Tongji University, where I built a solid foundation in programming, data analysis, and machine learning, complemented by interdisciplinary training in business and organizational systems.

My research interests focus on Urban AI, Social Sensing, Spatial Intelligence, and embodied AI in urban scenes. In particular, I am interested in how AI systems can perceive, reason, and interact with the world not only through data but also through space, behavior, and cognition.

Currently, I am focusing on:

(1) Mobility and accessibility of urban infrastructure for people with disabilities

(2) Urban perception and visual aesthetics to inform urban design and regeneration

(3) Large-scale AI-enhanced geospatial analysis of how urban environments and planning shape human behavior

(4) Multimodal urban data fusion for constructing virtual urban agents and digital twins for sustainable and equitable cities

(5) Embodied urban intelligence that coordinates interactions among autonomous vehicles, drones, and pedestrians

(6) Representation and embedding of geographic and spatial knowledge as a foundation for building world models

My research aims to build intelligent systems that are spatially and socially aware, providing computational solutions that make cities more adaptive, inclusive, and human-centric, while addressing complex societal challenges.

View My CV / Email / Github / Wechat / LinkedIn

🔥 News

  • 2025.12: 🎉 Our paper, led by my senior labmate Dr. Weihua Huan and co-authored with Prof. Wei Huang at Tongji University, was accepted by GIScience & Remote Sensing; honored to contribute as second author and big congratulations to Dr. Huan!
  • 2025.10: 🔭 Joined Prof. Yu Liu and Prof. Fan Zhang’s team at Peking University as a remote research assistant.
  • 2025.08: 🎉 Delivered an oral presentation at Hong Kong Polytechnic University after our paper was accepted to the Global Smart Cities Summit cum The 4th International Conference on Urban Informatics (GSCS & ICUI 2025).
  • 2025.07: 🎉 My undergraduate thesis was accepted by 7th Asia Conference on Machine Learning and Computing (ACMLC 2025).
  • 2025.06: 🎓 Graduated from Tongji University—grateful for the journey and excited to continue my studies at CMU.
  • 2025.04: 🔭 Completed the SITP project under the supervision of Prof. Yujia Zhai in the College of Architecture and Urban Planning.
  • 2025.01: 💼 Joined Shanghai Artificial Intelligence Laboratory as an AI Product Manager Intern.
  • 2024.09: 🌏 Conducted research at ASTAR in Singapore under the supervision of Dr. Yicheng Zhang and Dr. Sheng Zhang.
  • 2024.04: 🔭 Began my academic journey at Prof. Wei Huang’s lab in the College of Surveying and Geo-Informatics, Tongji University.

📝 Publications

XXV ISPRS Congress
sym

UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images

Kaizhen Tan, Fan Zhang

[abstract]

  • Under review at XXV ISPRS Congress 2026
  • Leverage street-view imagery and VGGT-based 3D reconstruction to estimate metrically scaled sidewalk widths, build the SV-SideWidth dataset, and fill OpenStreetMap gaps for equitable assessment of pedestrian infrastructure.
Urban Informatics
sym

A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai

Kaizhen Tan, Yufan Wu, Yuxuan Liu, Haoran Zeng

[arXiv] [slides]

  • Accepted at the Global Smart Cities Summit cum The 4th International Conference on Urban Informatics (GSCS & ICUI 2025)
  • Developed an AI-powered multimodal framework to analyze tourist perception in historic Shanghai quarters, integrating image segmentation, color theme analysis, and sentiment mining for heritage-informed urban planning.
ACMLC 2025
sym

Multimodal Deep Learning for Modeling Air Traffic Controllers Command Lifecycle and Workload Prediction in Terminal Airspace

Kaizhen Tan

[arXiv] [slides] [code]

  • Accepted at 7th Asia Conference on Machine Learning and Computing (ACMLC 2025)
  • Designed a multimodal deep learning framework linking ATCO voice commands with aircraft trajectories to model workload dynamics, enabling intelligent command generation and scheduling support.
GIScience & Remote Sensing
sym

A Spatiotemporal Adaptive Local Search Method for Tracking Congestion Propagation in Dynamic Networks

Weihua Huan, Kaizhen Tan, Xintao Liu, Shoujun Jia, Shijun Lu, Jing Zhang, Wei Huang

[arXiv] [slides]

  • Accepted at GIScience & Remote Sensing
  • Proposed a spatiotemporal adaptive local search (STALS) method combining dynamic graph learning and spatial analytics to model and mitigate large-scale urban traffic congestion propagation.

📖 Educations

  • 2025.08 – 2026.08: Carnegie Mellon University
    – M.S. in Artificial Intelligence Systems Management

  • 2021.09 – 2025.06: Tongji University
    – B.S. in Information Management and Information System

💻 Internships