Biomolecular Sequence Analysis
Mathematical models, statistical methods, computational algorithms, machine learning approaches are developed for nucleic acid and protein sequence analyses with the aim of predicting functional sites (e.g., replication origins, transcription factor binding sites, glycosylation sites).

Ecoinformatics and Phylogenetic Analysis
Ecological and evolutionary questions are addressed by molecular and bioinformatics techniques. Projects include studying the molecular phylogeny of the major families of Rotifera, assessing population differentiation using specific gene regions, and modeling fitness landscape by dynamical systems.

Enhancement of Bioinformatics Curriculum
Emphasis on designing a well-balanced bioinformatics curriculum for graduate students with diverse backgrounds to acquire new knowledge and skills in a cooperative learning environment. Development of interdisciplinary approaches for enhancing undergraduate engineering and science education.

Genomics and Proteomics Data Analysis
Classification and clustering techniques, Bayesian variable selection approaches, probabilistic Boolean network models, and wavelet methods are developed for analyzing genomics and proteomics data, with special interest in their applications to classification of diseases and modeling genetic regulatory networks.


Molecular Structure and Dynamics
Various mathematical optimization techniques, sequence segment sampling strategies, multi-scale algorithmic adaptations, and heterogeneous grid computing technology to predict structures for proteins and RNA molecules, as well as the molecular dynamics of protein-ligand docking.