Research Interests

We are interested in the development of signal processing and machine learning methods that can lead to an effective analysis of large-scale data, mathematical modeling, and control of complex systems. Main research topics include (but are not limited to) the following topics:

1. Large-scale Single Cell Sequencing Analysis

  • Development of algorithms to analyze large-scale single cell sequencing data.

2. Probabilistic Graphical Models and Algorithms for Computational Biology

  • Development of mathematical models for comparative network analysis algorithms and machine learning techniques for large-scale network analysis.

3. Biological Network Analysis

  • Development of algorithms for comparative analysis of large-scale biological networks such as protein-protein interaction (PPI) networks, gene regulatory networks (GRN), and co-expression networks.

4. Biological Sequence Analysis

  • Development of novel probabilistic models for biological sequence analysis algorithms for sequence alignments, RNA structural analysis, and protein homology search.

5. Large-scale Data Analysis and Heterogeneous Data Integration

  • Development of dimensionality reduction methods and mathematical models to analyze and integrate large-scale heterogeneous data.