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Researcher

Jeff Parker

Periodic LabsMember of Technical Staff

Parker develops AI simulations of complex physical systems. He co-developed DySLIM, which helps keep predictions reliable as small errors grow,[1] and studied how organized flows emerge from turbulence in plasmas—the hot, electrically charged gases used in fusion research.[2]

Career & education

Current role
Aug 2026-present
Member of Technical Staff
Previously
2023-2026
Software Engineer
2020-2021
Assistant Professor of Physics
Education

Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.

References

  1. DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemsarxiv.org

    The paper presents a method for training models to make more stable long-term predictions of complex physical systems. Parker is a co-author.

  2. Zonal Flow as Pattern Formationarxiv.org

    The paper connects organized flows in turbulence with pattern formation. Parker is a co-author.

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