Research

  • Computational Biology (Shehu) Lab

    Room : Engineering 4454 Web : http://cs.gmu.edu/~ashehu/

    Our lab focuses on developing algorithms to bridge between computer science and the life sciences. Our research contributions are in computational structural biology, biophysics, and bioinformatics. We investigate from a computational perspective problems concerning sequence, structure, dynamics, function, and interactions of biological molecules.
    Our methods build on probabilistic search, optimization, and machine learning approaches, often combining ideas from sampling-based robot motion planning and evolutionary computation.

    Predicted Structure of pB119L
    Predicted structure of pB119L (red) over known native structure (blue).

  • MLBio+ (Rangwala) Lab

    Room : Engineering 4416 Web : http://cs.gmu.edu/~hrangwal/

    My research interests include the areas of bioinformatics , chemoinformatics , data mining , and high performance computing . Within these areas, I have great interest in development of computational methods for proteins structure and function prediction, with implications in drug design and discovery.
    There is an emphasis on development of novel algorithms, and engineering of effective solutions. My research has resulted in development in useful and efficient software tools that aid biologists to make key discoveries, and some of the contributions have advanced the field of computer science. Please refer to the publications page of my web-site for learning more about my research.

  • EC (De Jong) Lab

    Room : Engineering Web : http://cs.gmu.edu/~eclab/

    At the simplest level, Evolutionary Computation (EC) involves the design and application of computational models of Darwinian-like models of evolution. Such models can be used to better understand existing evolutionary systems (such as bacterial resistance to penicillin) or can be used to make computer systems themselves more robust, flexible and adaptive (such as a stock market trading program). Here at the EC lab, we are interested in both kinds of applications but are more involved in the latter.
    Historically, a variety of evolutionary computation models have been developed including such well-known methods as Evolutionary Programming, Evolutionary Strategies, Genetic Algorithms, and Genetic Programming. These models have been successfully applied to a wide range of difficult science and engineering problems including innovative design, optimization, and machine learning. Here at EC lab we have researchers interested in all of these approaches, as well as developing new evolutionary models and applications.

  • Barbará Lab

    Room : Engineering Web : http://cs.gmu.edu/~dbarbara/

    Data Mining, Machine Learning, and their Applications

  • Domeniconi Lab

    Room : Engineering Web : http://cs.gmu.edu/~carlotta/

    Machine Learning, Data Mining, Pattern recognition, Classification, Clustering, Feature Relevance Estimation, Text Mining, Bioinformatics