I am an Assistant Professor (Boya Young Fellow) at the College of Urban and Environmental Sciences at Peking University. My research group focuses on AI-enabled geo-informatics for sustainability—integrating geospatial AI with physical principles and social theories to enable large-scale mapping and modeling of the interplay among infrastructure systems (e.g., energy infrastructure), climate threats, and human factors. Our goal is to (1) advance our understanding of the infrastructure-climate-human nexus and its spatial heterogeneity; (2) create next-generation information systems that transform raw pixels into actionable insights for accelerating decarbonization and improving climate resilience.

I am actively looking for PhD students (including one PhD student starting from 2026 Autumn), postdocs, and research assistants to join my group. If you are interested, please email me with your CV and a brief summary of your research interests.

Our current research thrusts include:

• Geospatial AI and information systems: Multi-modal machine learning foundation models for geospatial data; geospatial reasoning; AI-enabled information access and decison support systems.
• Infrastructure-climate-human nexus: Combining AI-based mapping, physics-based models, and social simulations to understand the emergent phenomena and spatial hetereogeneity of the infrastructure-climate-human coupled system.

Previously, I was a Human-Centered AI Postdoctoral Fellow at Stanford University. I obtained my PhD degree in Civil and Environmental Engineering with a PhD minor degree in Computer Science at Stanford, co-advised by Prof. Ram Rajagopal and Prof. Arun Majumdar. I obtained my M.S. degree in Mechanical Engineering from Stanford, and my Bachelor’s degree from Tsinghua University.

Email: zhecheng (at) pku (dot) edu (dot) cn

Recent News

• 09/2025 Joining the College of Urban and Environmental Sciences at Peking University as an Assistant Professor.

• 07/2025 Gave a talk at the MultiSector Dynamics (MSD) Community of Practice webinar.

• 05/2025 Our paper on LLM-enabled knowledge discovery of building-level electrification has been published in Energy and Buildings.

• 03/2025 Our paper on the newly updated DeepSolar database has received the Best Paper Award at the ICLR 2025 Workshop: Tackling Climate Change with Machine Learning.