Index Structures (Graduate)

Bioinformatics (Graduate & Undergraduate)

Machine Learning (CS 165B, undergraduate)

Artificial Intelligence (CS 165A, undergraduate)

Problem Solving with Computers II (CS24, undergraduate)

My research interests are broadly in the areas of network science, cheminformatics & bioinformatics, graph querying and mining, and databases (recent papers).

• Ph.D., Computer Science, The University of Texas at Austin , 1989

• M.S., Computer Science, Iowa State University, 1984

• B.Tech., Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 1982

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 the foundation, methodologies, algorithms, and implementations needed for effective, scalable, hierarchical, and most importantly, dynamic and resilient information networks. Specific problems include querying composite networks, modeling and mining dynamic networks, reasoning about composite network behavior, and mathematical models for trust in composite networks.

A number of scientific endeavors are generating data that can be modeled as graphs: high-throughput biological experiments on protein interactions, high-throughput screening of chemical compounds, social networks, ecological networks and food webs, database schemas and ontologies.  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.

Information technology research has played a significant role in the genomics revolution over the past decade, from aiding with large-scale sequence assembly to automating gene identification to efficiently searching databases by sequence similarity.  The tremendous amount of information gathered from genomics will be dwarfed in the next decade by the knowledge to be gained from comprehensive, systematic studies of the properties and behaviors of all proteins and other biomolecules. High resolution imaging of molecules and cells will be critical for understanding complex systems such as the nervous system, whether it be for the localization of specific neuron types within a region of the central nervous system, the branching pattern of dendritic trees, or the localization of molecules at the subcellular level. Further, knowing how these distribution patterns and subcellular locations change as a function of time is critical to understanding how cells respond to stress, injury, aging and disease. 

The Center for BioImage Informatics brings together Biologists, Computer Scientists and Engineers in interdisciplinary research that will not only advance the state of the art in imaging, pattern recognition, and data mining, but also will result in a better understanding of complex biological processes at the cellular and sub-cellular level.  We have developed 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.

Increased availability of large repositories of chemical compounds has created new challenges and opportunities for the application of data-mining techniques to problems in chemical informatics. Applications of chemoinformatics lie in performing virtual high-throughput screening to identify active compounds in a virtual library and predicting structure-activity relationships. Typical querying and mining tasks involve clustering of large molecular libraries, developing index structures for fast answering of top-k searches, identifying statistically significant molecular substructures, and predicting biological activity of molecules.

Research Projects