Kexin Song

Kexin Song

Ph.D. | Remote Sensing & Geospatial AI & Forest Ecosystem Ecology

Teaching Philosophy

I view teaching as an opportunity to equip students with the conceptual foundations, technical skills, and critical thinking abilities needed to address complex environmental challenges. My approach emphasizes student motivation, hands-on learning, and ethical use of artificial intelligence as a learning tool rather than a substitute for reasoning.

I connect course content to real-world applications, incorporate active learning strategies such as project-based labs and group discussions, and provide visual and interactive materials to support students from diverse backgrounds.

Teaching Experience

Teaching Assistant / Lab Instructor

Remote Sensing of Environment (NRE 3535)
University of Connecticut | Fall 2023

  • Led weekly laboratory sessions on satellite data acquisition, visualization, and analysis using ENVI.
  • Delivered guest lectures on multispectral remote sensing and satellite geometry.
  • Graded assignments and provided constructive feedback to support learning.
  • Organized group discussions and student presentations.

Research Mentorship

  • Mentored undergraduate and graduate students on projects involving satellite time-series analysis, machine learning, and forest disturbance monitoring.

Future Teaching Interests

I am interested in teaching undergraduate and graduate courses in:

  • Environmental Data Science, Geospatial AI, and Generative AI
  • Remote Sensing and Earth Observation
  • Geographic Information Systems (GIS) and Spatial Analysis
  • Forest Ecology, Disturbance, and Resilience
  • Human–Environment Interactions and Land System Science

I am also enthusiastic about developing interdisciplinary courses that integrate satellite remote sensing, generative AI, machine learning, and reproducible research workflows using Python, GIS, and cloud-based geospatial platforms. These courses would emphasize how Earth observation and AI can be used to understand ecosystem dynamics, monitor environmental change, and support evidence-based decision-making for natural resource management, conservation, and climate resilience.