Our research focuses on network science, scalable querying and mining of graphs, and bioinformatics.
Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior. The National Research Council defines Network Science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." My group is developing methodologies, algorithms, and implementations needed for scalable, dynamic, and resilient networks. Specific problems include querying composite networks, modeling dynamic networks, sentiment analysis, analysis of content and user behavior, discovering unusual patterns, and sampling in composite networks.
A number of scientific endeavors are generating data that can be modeled as graphs: high-throughput genome analysis, screening of chemical compounds, social networks, and ecological networks and food webs. Mining and analysis of these annotated and probabilistic graphs is crucial for advancing the state of scientific research, accurate modeling and analysis of existing systems, and engineering of new systems. The goal of this research project is to develop a set of scalable querying and mining tools for graph databases by integrating techniques from the fields of databases, bioinformatics, machine learning, and algorithms.
Intensive investigations over several decades have revealed the functions of many individual genes, proteins, and pathways. There has been an explosion of data of widely diverse types, arising from genome-wide characterization of transcriptional profiles, protein-protein interactions, genomic structure, genetic phenotype, gene interactions, gene expression, and proteomics. We are developing techniques that can integrate and analyze data from multiple sources and models efficiently. One research thrust quantifies phenotypic variation using image analysis and pattern recognition tools, develops a causal model for gene regulatory processes, and validates the model experimentally. In another research thrust, high resolution images of molecules and cells are being analyzed for understanding complex systems such as localization of specific neuron types, branching patterns of dendritic trees, and localization of molecules at the subcellular level. These efforts are being augmented by a unique distributed digital library of bio-molecular image data. Such searchable databases will make it possible to optimally understand and interpret the data, leading to a more complete and integrated understanding of cellular structure, function and regulation.