Teams|

Performance and Architecture Laboratory (PAL)

The Performance and Architecture Laboratory (PAL) 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.

Machine Learning & Pattern Recognition Team

The Machine Learning & 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.

Knowledge and Information Systems Science Team

The Knowledge and Information Systems Science team performs scientific research in computational methods for the extraction, representation, organization, synthesis, discovery, and retrieval of knowledge in databases and information systems. We especially emphasize methods for representing semantic information, and hybrid methodologies combining statistical, numerical, and quantitative with symbolic, logical, and qualitative techniques. Specific technical areas of expertise include:

Discrete mathematical, combinatorial, and order theoretical approaches to ontological and conceptual semantic systems;

Computational linguistic and lexical semantic approaches to natural language processing and text extraction;

Generalized and hybrid approaches to uncertainty quantification and information theory; and

Software architectures for such techniques emphasizing parallel and high performance methods.

Our primary application area is computational biology, and thus we have a close working relationship with the Computational Biology and Bioinformatics team. Other application areas include homeland defense, critical infrastructure protection, intelligence analysis, decision support environments, risk and reliability analysis, and defense transformation.

Quantum and Classical Information Science Team

The Quantum and Classical Information Science 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 CCS-3's primary interest in data mining, information science and complex systems modeling.

Solvers Team

The CCS-3 Solvers Team specializes in the development of scalable solution methods for linear and nonlinear problems, and their efficient implementation on high-performance computing systems for use in large-scale scientific and engineering applications. As part of the broader Solvers Project at LANL, our current focus areas include Newton-Krylov methods, multilevel methods for problems on locally-refined grids, and multilevel overlapping Schwarz methods.

Information Physics & Modeling Team

The Information Physics & Modeling Team is concerned with the development of novel techniques for the analysis and simulation of large scale, complex systems. We have an active research program in several areas, including estimation theory, nonlinear dynamics, accelerated and hybrid numerical methods, and network modeling, as well as both fundamental and applied statistical mechanics. Our team has strong collaborations across LANL, at other National labs and at universities throughout the United States.

Computational Biology & Bioinformatics Team

The Computational Biology & Bioinformatics Team seeks new quantitative and predictive descriptions of biological systems, through the integration of mathematical analysis, advanced computing and empirical data. Active research areas include developing methods to predict protein function from genetic sequences, atomic structures, and text information, and analysis and modeling of genetic regulatory networks in bacteria. Our research has implications for understanding the genetic encoding of cellular behavior, information processing in biological systems, and design principles in biology and beyond. We maintain a wide range of collaborations and have especially close ties to the Knowledge and Information Systems Science <http://www.c3.lanl.gov/knowledge/> team in CCS-3, and the Cell Signaling <http://cellsignaling.lanl.gov/> team in T-10.

CCS-3 Internal