We are interested in developing probabilistic graphical models, machine learning techniques, and signal processing schemes that can be applied to effective analysis of large-scale biological data and mathematical modeling, simulation, analysis, and control of complex biological systems.
Main research topics include (but are not limited to) the following.
1. Probabilistic Graphical Models & Algorithms for Computational Biology
Application of hidden Markov models (HMM), Bayesian networks, and Markov networks for systematic analysis of biological systems and data.
2. RNA Sequence Analysis & Identification of Noncoding RNAs (ncRNA)
Models and algorithms for structural RNA alignment, RNA similarity search, and identification of novel noncoding RNA (ncRNA) genes.
3. Biological Network Analysis
Development of algorithms for comparative analysis of large-scale biological networks, including protein-protein interaction (PPI) networks, metabolic networks, and co-expression networks.
4. Gene Expression Analysis for Accurate Disease Diagnosis /Prognosis
Systems-based approach for robust and accurate classification of complex diseases, such as cancer, through integration of gene expression data, protein interaction data, and prior knowledge (e.g., biological pathways).
5. Bayesian Learning, Optimization, and Optimal Experimental Design Under Uncertainty
Development of an effective Bayesian framework and computational methodologies for optimal integrative learning based on scientific knowledge and data, optimal robust operator design, and optimal experimental design for complex systems under uncertainty.