Ph.D., Computer Science, The University of Texas at Austin, 1989
M.S., Computer Science, Iowa State University, 1984
Computer Science and Engineering, Indian Institute
of Technology, Kharagpur, 1982
My research interests are broadly in the areas of network science, cheminformatics & bioinformatics, graph querying and mining, and databases (recent papers).
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.
Increased availability of large repositories
of chemical compounds and other biochemical data has created new
challenges and opportunities for data-mining in chemical
informatics and drug discovery: identification of active
substructures and compounds, prediction of physicochemical
properties and structure-activity relationships, diversity
analysis of compound collections, drug repurposing, and pathway
mining for identification of network fragments responsible for
disease progression. My group has developed several graph-based
and 3D-based methods for such analyses. These ideas are being
pursued by Acelot, Inc., a
local drug discovery startup.
Past Research Group