Data Mining in Environment

Owing to the rapid development of information technology and data acquisition devices, we have come across an era where data grows at exceptional speed in both volume and complexity. Correspondingly, so large have the quantities grown that the world immediately demands new tools and methods to analyze it and reveal the underlying patterns for further exploitation.

Today’s environmental problems think climate change, water/air/soil/sea water quality and availability, human health, forest and wildlife conservation are increasingly complex and involve vast amounts of data. Environmental informatics majors can help address these challenges by mastering the methods and developing the tools needed to translate complex data into usable facts and figures.

The EIBL is going to develop analytical, decision making, and data modeling skills that are critical for the environmental problems and for pursing ongoing research to solve complex problems. Our research interests lie in artificial intelligence (AI), data mining, and machine learning to find models, algorithms, and systems for effective monitoring of environmental contamination using combining physical, chemical, and biological data. Specifically, we focus on the following research topics: deep learning & machine learning to improve the knowledge quality of environmental monitoring and management.