CIC-3 is composed of teams, which bring together at a finer level group members having common research interests. They also serve as the lowest level of organizational structure.
The Parallel Architectures and Performance Team specializes in performance analysis, modeling and engineering of large-scale parallel systems and applications. We are developing and using a broad spectrum of methodologies such as analytical modeling, simulation, queuing theory and experiment for characterizing these complex systems.
The Machine Learning and Pattern Recognition Team performs research in several diverse machine learning and pattern recognition areas. We develop algorithms for statistical and adaptive pattern recognition, as well as image and signal processing. These algorithms are applied to a variety of data mining and knowledge discovery applications.
The Distributed Knowledge Systems and Modeling research team is concerned with the theory of knowledge systems and the modeling of systems from a knowledge-based perspective. Our goal is to help develop a scientific understanding of such systems, and apply this to areas of vital interest to the Laboratory and the nation, including the organization and management of scientific knowledge systems, in possible in a secure environment; protection of the information infrastructure; and national security and intelligence.
Our primary research interest is in Distributed Knowledge Systems (DKS): communities of agents (human and computational) interacting with networked information resources. Examples include the Internet, enterprise-level Intranets, digital libraries, and other interactive knowledge repositories. We also view agent-based modeling and biocomputation from a knowledge-based perspective.
The Fundamentals of Computing Team is involved in basic and applied research on computational problems arising in diverse areas, including software engineering, theory of computing, modeling and simulation and signal processing. Academic strengths of the team include both traditional and quantum information theory, discrete mathematics, software design and engineering and theoretical computer science. The team's mission is to provide a theoretical and programmatic competency for CIC-3's primary interest in data mining, information science and complex systems modeling.
The CIC-3 Linear Solver Team specializes in the design, implementation and analysis of linear solver algorithms and codes for use in large-scale modeling and simulation applications. Our clients include researchers at Los Alamos National Laboratory and other national laboratories, government agencies, and industry, who require high-performance solvers to model complex physical phenomena on high-end parallel computers.
The Numerical Analysis and Parallel Computing Team is devoted to numerical algorithms, solutions, and validations and verifications of problems of interest to the Laboratory. Its members have diverse backgrounds in mathematics, engineering, computer science, and physics. Current research is conducted in areas of numerical algorithms and mathematical analyses, with applications to problems such as ocean modeling, spectral finite element methods, stochastic partial differential equations, signal analysis, porous media flows, upscaling, mesoscopic phenomenology, and nonequilibrium sciences.