Our research is maily focused on:
Nowadays, effective analysis of complex information and big data heavily relies on solid visualization technologies in numerous areas from life sciences to software engineering to social networks. Hence, we also aim to produce reusable software components based on our research results to make generic and domain-specific tools better in storing, quering, laying out, editing, managing complexity of, and viewing relational information in big data.
- Big data analytics and visualization (esp. biological pathways and social networks) including complexity management in large networks,
- Bioinformatics (pathway informatics) including efficient and user friendly tools for constructing, visualizing and analyzing networks,
- Graph algorithms (esp. graph layout algorithms and query algorithms for graph databases) including automatic layout of compound (nested) and clustered (grouped) networks and extracting a graph of interest from objects of interest in graph databased through effective queries,
- Machine learning (detecting fraud/anomaly in financial networks) including clustering objects or predicting missing relations in networks.
Some demos and tutorials on our methods and tools can be found here in YouTube.