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, Digital Twins, 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 self-evolving digital twins and virtual urban agents for sustainable and equitable cities
(5) Embodied intelligence-friendly urban spaces that coordinate interactions among autonomous vehicles, drones, robots, and pedestrians
(6) Representation and embedding of geographic and spatial knowledge for building urban foundational models
(7) Interpretable and 3D-enhanced VLM spatial reasoning with unified world representation and long-term memory
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.
🔥 News
- 2026.01: 🎉 The abstract co-authored with Prof. Fan Zhang has been accepted for the XXV ISPRS Congress 2026. See you in Toronto!
- 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
UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images
Kaizhen Tan, Fan Zhang
- Accepted 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.
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
- 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.
Multimodal Deep Learning for Modeling Air Traffic Controllers Command Lifecycle and Workload Prediction in Terminal Airspace
Kaizhen Tan
- 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.
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
- Published in GIScience & Remote Sensing (JCR Q1; IF = 6.9).
- 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.
🔬 Project
BlindNav: YOLO+LLM for Real-Time Navigation Assistance for Blind Users
Kaizhen Tan, Yufan Wang, Yixiao Li, Hanzhe Hong, Nicole Lyu
- BlindNav is a real-time, camera-based navigation assistant that uses YOLO for street-scene detection and a local LLM to turn those signals into concise voice guidance for blind and low-vision pedestrians.
📖 Educations
💻 Internships
- 2025.01 - 2025.04, AI Product Manager, Shanghai Artificial Intelligence Laboratory, China.
- 2024.09 - 2024.12, Research Officer, A*STAR Institute for Infocomm Research, Singapore.
- 2023.01 - 2023.02, Data Analyst, Shanghai Qiantan Emerging Industry Research Institute, China.