Environment/Climate

The network structure and evolution over time provide interesting insights into the behavior of the earth system. For example, ocean climate indicators extracted from the networks have proven to be good predictors of climate variables over land. Currently, we are studying the dynamic behavior and stability of the network over time

ND-GAIN Index

The ND-GAIN Index, a project of the University of Notre Dame Global Adaptation Index (ND-GAIN), summarizes a country's vulnerability to climate change and other global challenges in combination with its readiness to improve resilience. It aims to help businesses and the public sector better prioritize investments for a more efficient response to the immediate global challenges ahead.

Personnel: 

Please visit index.gain.org for more information

Ecological Niche Modeling

My research interests focus on the interdisciplinary applications of data mining and machine learning to large and imbalanced datasets. This work particularly focuses on applying machine learning methods to modeling and predicting species' ecological niche models, a common problem in ecological informatics. The broad idea is to use abiotic variables—often environmental in nature—at known species locations to generate predictions for species occurrences at other locations, based upon these variables.

Patterns of Ship-borne Species Spread: A Clustering Approach for Risk Assessment and Management of Non-indigenous Species Spread

The spread of non-indigenous species (NIS) through the global shipping network (GSN) has enormous ecological and economic cost throughout the world. Previous attempts at quantifying NIS invasions have mostly taken "bottom-up" approaches that eventually require the use of multiple simplifying assumptions due to insufficiency and/or uncertainty of available data. By modeling implicit species exchanges via a graph abstraction that we refer to as the Species Flow Network (SFN), we pursue a different approach that exploits the power of network science methods in extracting knowledge from largely incomplete data.

Image: Major clusters of species flow network during 2005-2006: Major clusters remain largely unchanged for the duration of 1997-2006, and contain a significant proportion of total species flow between ports.