About Me

Minho Kim

I’m a PhD candidate in Environmental Planning at UC Berkeley advised by Professors Marta C. Gonzalez and John Radke. My research focuses on building data-driven methods to model and predict natural hazard risks on our built and natural environment. To this end, I use GeoAI methods, computational modeling, and spatially embedded networks to develop decision support tools that guide risk-informed decisions, improve risk management, and build resilience in complex socio-ecological systems like the Wildland Urban Interface.

I received my BS and MS degrees in Civil and Environmental Engineering at Seoul National University, where I was advised by Professor Yongil Kim and a member of the SPINS Lab.

Education

  • PhD in Environmental Planning, UC Berkeley (2026 Expected)
    Dissertation: Data-Driven Planning for Resilience Against Natural Hazard Risks
  • MS in Civil & Environmental Engineering, Seoul National University (2021)
    Thesis: Local Climate Zone Classification Using Multi-Scale Convolutional Networks
  • BS in Civil & Environmental Engineering, Seoul National University (2017)
    Thesis: Monitoring North Korea’s 4th Nuclear Test Site Using Sentinel-1A DInSAR

Research

As an environmental planner, civil engineer, and geospatial data scientist, my research centers on leveraging novel datasets and emerging technologies to protect and adapt our built and natural environments against natural hazard risks and system disruptions. I combine computational models (fire spread simulations), spatially embedded networks, and GeoAI methods to create scalable decision-support tools for risk management and to generate critical earth intelligence that informs strategic planning and policy. My aim is to translate complex data into actionable insights, fostering resilience and proactive adaptation in the face of mounting environmental challenges. My research is driven by three core themes:

  • (1) Developing computational models to simulate and predict natural hazard risk
  • (2) Mapping earth informatics and intelligence through advanced geospatial data science and GeoAI
  • (3) Building intelligent decision-making support for complex systems under uncertainty.

Awards