The Computational Sciences & Mathematics Division (CSMD) provides creative scientific and technological solutions to challenges in problems of national and global importance by pursuing a robust portfolio of fundamental and applied research at the frontiers of computational science, computer science, and mathematics.
- Computational Biology & Bioinformatics
Advances in the computational modeling and simulation of complex biological systems are transforming biological research from a qualitative, descriptive science to a quantitative, predictive science. PNNL's focus is on the design and efficient implementation of computational capabilities for the analysis of data from high-throughput experimental technologies, the abstraction of models from this data, and the predictive simulation of these models. Beyond the validation of experimental observations, these simulations enable the design and prediction of the outcome of new experiments. This is an essential part of the scientific discovery cycle aiming at the development of technical approaches to bioremediation, bioenergy production, and climate management.
POC: Katrina Waters
- Computational Mathematics
Leveraging mathematical models to quantify and control scientific uncertainty to further scientific discovery. Scientific research and development is a process of gaining fundamental understanding of physical, chemical, and biological principles through computational modeling, experimentation, and data evaluation. As a leader in applied mathematics research, we develop novel data-analysis methods to extract hidden features, anomalies, and signatures from high-dimensional, large-volume, multimedia data in support of discovery and confident decision-making. We develop methods and tools to optimize data-gathering approaches through sampling and experimental design.
POC: Dave Engel
- Data Intensive Scientific Computing (DISC)
Creating advanced software tools and reusable frameworks for data intensive science. Modern scientific systems are highly data intensive and are required to process, analyze, and manage massive quantities of heterogeneous data. In DISC, we created reusable software frameworks, platforms, and tools to enable scientists to manage experimental data, set up complex models, execute large-scale simulations on high-performance computing platforms, and analyze the results. The innovations we develop affect a range of software technologies, including advanced graphical user interfaces, distributed software integration and workflow frameworks, customizable knowledge management platforms for modeling and simulation, and system-level tools for high-performance data capture and processing. We apply these technologies in many scientific and engineering domains, including subsurface modeling, carbon sequestration, bioinformatics, climate modeling, and the power grid.
POC: John Feo
- High Performance Computing
Employing high performance computing to solve scientific problems by developing and implementing high-level programming abstractions and high-speed networks and communication tools. Our approach merges science and technology by 1) employing hardware that maximizes processor speed, memory and interconnect bandwidth, efficient use of secondary storage, and reliability; 2) developing algorithms that are scalable, resource-efficient, and load-balanced and that manage computational complexity and exploit space-time locality; and 3) creating programming models, numerical libraries, communication libraries, compilers, and debuggers that support data decomposition, low communication overhead, and portability. We are also leading providers of problem-solving environments that increase ease of use and availability of high-performance computing to nonspecialists.
POC: Darren Kerbyson
- Scientific Data Management
Enabling Data Intensive Science through advanced scientific data management methodologies and techniques. We conduct research and technology development, as well as delivering innovative solutions and services that address the challenges arising in data intensive sciences, with a particular focus on environments with extreme scale, highly diverse data. We emphasize architecting integrated solutions from data capture to knowledge generation, closely embedded into the scientific research processes. These solutions are facilitated in particular through data quality management, metadata, provenance, semantic technologies and knowledge management systems. They offer data capture, annotation, storage, access, assessment, analysis, multi-source integration, data sharing, publication and curation. Our work is strongly driven by the current and future needs of data intensive science communities and we are working closely with researchers from observational and computational climate and earth systems research, systems biology, chemical imaging and simulations, particle physics and energy sciences experiments and simulations, as well as the law enforcement and intelligence community.
POC: Kerstin Kleese van Dam