Mathematics for Artificial Reasoning in Science initiative lead
Mathematics for Artificial Reasoning in Science initiative lead


At Pacific Northwest National Laboratory (PNNL), Mark Raugas has contributed to the artificial intelligence (AI) rapid internal investment, with a focus on applying AI techniques to research in static and dynamic program analysis and the analysis of the security of hardware architectures used in AI.

Raugas has led research activities in high performance data analytics—a portfolio including research in topological data analysis, graph analytics, hypergraph analytics, novel high-performance computing (HPC) networks and software runtimes for shared and distributed memory programming models of HPC—including work maturing the Rust programming language for use in HPC.

Raugas was involved in the joint Department of Energy/Department of Defense (DoD) Project 38 activity focused on heterogeneous computing to support purpose-built processors and accelerators for scientific and analytic workloads of interest for scientific discovery and national security. Raugas also served as the HPC system software lead and then later as the Chief Scientist of the PNNL Data Model Convergence Laboratory Directed Research and Development initiative, advancing the state of the art of computing in a converged, post-Moore’s law era.

In September 2021, Raugas began an Intergovernmental Personnel Act assignment from PNNL to the DoD as the Technical Director of the Laboratory for Physical Sciences (LPS) in College Park, Maryland. In that role, Raugas coordinated the strategic technical direction of physical sciences research activities at LPS, including research in materials science, advanced sensors, microelectronics, semiconductors, solid-state physics, HPC, quantum physics, and quantum information sciences.

Raugas returned to PNNL in September 2022 and currently leads the Mathematics of Artificial Reasoning in Science (MARS) initiative.

Disciplines and Skills

  • Geometry
  • Topology
  • Physics
  • HPC
  • Machine Learning
  • Artificial Intelligence
  • Computer Architecture
  • Program Analysis


  • PhD in Mathematics, Columbia University, MA., MPhil, 2001
  • BA in Mathematics with Honors, BA in Physics, New York University, 1996
  • Regents Diploma with Honors, the Bronx High School of Science, 1992

Affiliations and Professional Service

  • Senior Member, Association for Computing Machinery
  • Member, External Advisory Board, University of Maryland Cybersecurity Center 

Awards and Recognitions

  • Alumni Scholar, NYU 
  • Magna Cum Laude, NYU 
  • Graduate Fellowship, Columbia University