Computer Science and Applied Mathematics

The Computer Science and Mathematics Department focuses on making sense of large-scale data, including those output by major scientific facilities. To manage such diverse high-volume data, researchers are developing extreme-scale and streaming machine learning capabilities, as well as on-the-wire processing algorithms—all augmented by research on programmingmodels, compilers, performance analysis, modeling/portability, and network protocols.

Applied Mathematics Group

Applies mathematics to model and solve real-world problems. The team consists of mathematicians, statisticians, engineers, and physicists who work together and with domain scientists on large-scale, interdisciplinary challenges. Research areas include climate change, materials science, accelerator science, biomedicine, plant pathology, energy systems, and quantum computing.

Machine Learning Group

Focuses on advancing machine learning algorithms and their scientific applications. The group enables scientific breakthroughs by developing novel and scalable machine learning algorithms. It conducts cutting-edge research in areas such as computer vision, natural language processing, streaming and edge computing, and scalable and distributed computing. The group emphasizes machine learning algorithms’ interpretability, trustworthiness, responsiveness, and transparency.

Projects

• Analysis on the Wire (AoW)
• Automatic Parallelization and Optimization for Lattice QCD Software using a Source-to-Source Compiler
• Exascale Application Development for Lattice Quantum Chromodynamics
• Extreme-Scale Computing for Exascale and Strong-Scaled Problems (XPRESS)
• GPU Research Center
• Integrated End-to-End Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD)
• Mesoscale Models for Brittle Fracture of Solids
• Multicore-Aware Data Transfer Middleware: Harnessing Multicore Parallelism to Scale Data Movement
• SOLLVE: Scaling OpenMP with LLVm for Exascale performance and portability
• Software Defined Network Science Flows
• Visualizing and Interacting with Incremental Machine Learning Processes