Center for Applied Scientific Computing
Informatics Group

The members of the Informatics Group perform research and development in the broad area of information management and analysis. Research topics include the development of new algorithms in bioinformatics, data management for large-scale attributed graphs, reconfigurable hardware acceleration for data-intensive computing applications, novel approaches for analyzing large collections of text, analysis and understanding of dynamic networks, streaming algorithms, and cybersecurity. Many of our problems involve very large data sets, such as text collections of tens of millions of documents, graphs with billions of edges, or streaming cyber data at hardware line speeds. We typically employ a variety of technologies and tools, such as machine learning and classification algorithms found in packages like WEKA, hardware such as the Tilera multicore engine, streaming middleware such as IBM’s Infosphere Streams, and open-source tools such as Hadoop and SOLR.

The customers of the Informatics Group include scientists and analysts both at the Laboratory and in the US Government. Many of our members work with our Global Security Directorate, and we receive external funding from the DOE Office of Science, DARPA, and various other US Government agencies.

Group Lead

Brian Van Essen: spatial accelerators for embedded systems and high performance computing, reconfigurable computing, and memory architectures for data-intensive computing

Research Staff

David Buttler: database technology, web data access, web service selection, web document change detection

Ya Ju Fan: optimization models and algorithms, data classification and clustering, dimensionality reduction

Brian Gunney: adaptive mesh refinement, high-performance computing, numerical methods for partial differential equations, computational physics

David Hysom: combinatoric and discrete algorithms, scalable parallel methods, bioinformatics

Sam Ade Jacobs: parallel computing, large-scale graph (data) analytics, machine (deep) learning, and robotics

Chandrika Kamath: data mining, machine learning, signal and image processing, high-performance computing

Scott Kohn: cybersecurity, graph data management and analysis, high-performance computing

Scott Lloyd: high-performance computing applied to the physical and life sciences, computer architecture, reconfigurable computing

Celeste Matarazzo: cybersecurity, distributed agents, distributed sensor analysis

Roger Pearce: distributed file systems and tools to profile the I/O performance of data intensive applications

Jae-Seung Yeom: data-dependent application behavior modeling, performance analysis, epidemic, viral evolution simulations

Andy Yoo: scalable large graph algorithms, large-scale data management, data mining and knowledge discovery, high-performance parallel computing, parallel algorithms