Siva Rajamanickam

Siva Rajamanickam

Researcher

Sandia National Laboratories

Siva Rajamanickam is a senior AI systems and infrastructure leader and distinguished member of technical staff at Sandia National Laboratories, with extensive experience building AI models, infrastructure, systems, and governance frameworks across the U.S. National Nuclear Security Complex and the Department of Energy (DOE). He leads the BANYAN Generative AI Institute, developing foundation models for engineering systems relevant to nuclear weapons stewardship, and leads the development of the first federated language model across all three NNSA laboratories using restricted datasets. His research spans artificial intelligence and high-performance computing — specifically AI for science and engineering, AI infrastructure and hardware, performance-portable algorithms, co-design of algorithms and architectures, and combinatorial scientific computing.

Siva has directed large, cross-institutional teams executing complex, high-risk initiatives spanning advanced AI system development, national security risk assessments, safety evaluation, and large-scale infrastructure deployment. These work enable the development of secure, scalable AI platforms, models, and infrastructure that support U.S. national security objectives.

In general, his work operates at the intersection of innovative ideas, real-world consequences, and demanding timelines, with stakeholders across national laboratories, federal agencies, industry, and academia.

Interests
  • AI for Science and Engineering
  • AI Systems and Infrastructure
  • High Performance Computing
  • Performance Portable Algorithms
  • Codesign of Algorithms and Architectures
  • Combinatorial Scientific Computing
Education
  • PhD in Computer Science and Engineering, 2009

    University of Florida

  • BE in Computer Science and Engineering, 1999

    Madurai Kamaraj University

Recent Publications

Trilinos: Enabling scientific computing across diverse hardware architectures at scale
ShyLU-node: On-node scalable solvers and preconditioners: Recent progress and current performance
Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware
Beyond Exascale: Dataflow Domain Translation on a Cerebras Cluster

Projects

Instead of listing all the funded projects that I lead, this is a summary of all the areas I am interested in. All my projects fall into one or more of these areas.

Contact