The Advanced Computing, Mathematics, and Data (ACMD) Division, led by Robert Rallo, focuses on basic and applied research in computing and mathematics.  Our research programs are focused on intelligent systems, high-performance computing, computational mathematics, and software and data engineering. Our scientists develop and apply novel methodologies for:

  • Software Engineering for Data Integration, Visualization, and Analytics
  • Multi-Scale Modeling of Complex Systems
  • Uncertainty Quantification
  • Graph and Data Analytics
  • Autonomous Machine Intelligence
  • High-Performance Computing
  • Domain-Aware and Physics-Informed Machine Learning
  • Co-Design to Advance Computing Frameworks

Scientists and engineers in ACMD apply their expertise in mathematics, algorithms, hardware-software co-design and AI to revolutionize scientific discovery, advance computing systems, and accelerate quantum information science.

Software and Data Systems Engineering Group

Group Lead: Angela Norbeck

The Software and Data Systems Engineering group tackles the challenges associated with scientific data management, software and architecture development, and cloud computing. The group designs analytical platforms and user access portals for large and small scale data sources and distributed teams. Our scientists and engineers design and manage data flows at scale, develop web portals to access data, create interactive data visualizations, and leverage hybrid data management architectures to create robust and efficient data solutions.


  • Data Services
  • Infrastructure and Systems Deployment
  • Scientific Software Engineering

Computational Mathematics Group

Group Lead: Panos Stinis

Our Computational Mathematics group develops and enables innovative modeling and simulation methodologies that improve the understanding of complex systems. Our core capabilities include multi-scale and multi-fidelity approaches, uncertainty quantification, scientific machine learning and discrete mathematics for forward and inverse problems. In collaboration with academia, the group has pioneered the concept of physics-informed, data-driven methods and their application to problems in challenging domains, such as subsurface transport and ice-sheet modeling. The group also leverages its modeling and simulation capabilities to develop digital twins that will guide the design and operation of next-generation energy storage systems.


  • Algorithms, Combinatorics, and Optimization
  • Multiscale Modeling
  • Scientific Machine Learning and Uncertainty Quantification

Data Sciences and Machine Intelligence Group

Group Lead: Mahantesh Halappanavar

Our Data Sciences and Machine Intelligence group performs research at the intersection of computer science, data science, and AI. Our capabilities include data architectures, statistical ML, network science, graph analytics, AI, combinatorial optimization, design and control of complex systems, decision sciences, and scalability of methods through high-performance computing. Our scientists work closely with subject matter experts in computational biology, complex infrastructures, high energy physics, computational chemistry, atmospheric sciences, building technologies and the electric power grid, and cyber security, among other domains of national interest.


  • Artificial Intelligence and Data
  • Auto Learning and Reasoning
  • Scalable Analytics and Decision Optimization

High-Performance Computing Group

Group Lead: Kevin Barker

Our High-Performance Computing group conducts fundamental computer science research pertaining to hardware/software codesign; hardware design automation and synthesis tools; advanced computer architecture testbeds, assessment, and evaluation; runtime software tools including compilers; large-scale system performance prediction and analysis; ML and AI; and data-intensive system design and optimization.. Our focus is on hardware/software codesign methodologies to support new computing workloads and advanced accelerator design. Deep expertise in performance modeling methodologies have influenced the design of current and future large-scale computing systems and applications, while expertise in ML and  data-intensive systems are helping to shape the next generation of computational and data science capabilities for PNNL.


  • Parallel and Distributed Information Technology
  • Scalable and Emerging Technology
  • Scalable Computing and Data