Discover the Future of
Ecological Informatics

Bridging data science frameworks with ecological research to build a sustainable environment for tomorrow.

Learn More

About Ecoinfo

Satellite view of the Amazon River Estuary

Pioneering Ecological Research

The Ecoinfo Lab applies cutting-edge technology and data analytics to multifaceted ecology, tackling major challenges facing nature today.

Through artificial intelligence, machine learning, and big data techniques, we provide innovative solutions to identify species distributions, ecosystem responses to climate change, and conservation biology values.

  • ✔ Ecological Data Modeling & Prediction
  • ✔ Climate Change Impact Assessment
  • ✔ Biodiversity Conservation Algorithms

Current Projects

Core projects integrating the latest Information and Communication Technology (ICT)

Recent Research

Prominent papers published in world-leading ecological journals

Nature Ecology & Evolution 2026

Deep Learning Frameworks for Enhancing Precision in Species Range Predictions

Proposing a novel framework to model nonlinear climate change factors, overcoming the limitations of traditional SDMs.

Kim, J., Lee, S., & Ecoinfo Lab
Global Change Biology 2025

Long-term Resilience of Temperate Forests under Extreme Droughts

Analyzing drought stress responses of global temperate forests by correlating 30 years of satellite and meteorological data.

Park, H., Kim, J., et al.
Ecological Informatics 2025

Automated Audio Classification of Endemic Anura using Convolutional Neural Networks

Classifying calls of endemic amphibians with >95% accuracy under background noise using convolutional neural networks.

Choi, M., & Kim, J.

Courses & Lectures

Academic courses offered by our lab throughout the year

Spring Semester

Introduction to Ecological Informatics

A foundational course bridging the gap between computational sciences and modern ecology. Students learn basic programming, data wrangling, and fundamental ecological models.

Fall Semester

Advanced Spatial Data Modeling

An advanced dive into GIS, remote sensing, and machine learning techniques tailored for geographic and species distribution forecasting.

Graduate Course

Big Data in Conservation Biology

Focused on handling massive ecological datasets including audio, camera trap imagery, and genetic information to formulate conservation strategies.