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Machine Learning for Correlative and Analytical Measurements

FOCUS · V60 · ID: 380782

Presentations

  • Sequential Bayesian experimental design for everyday measurements

    ORAL

    –

    Presenters

    • Robert McMichael

      National Institute of Standards and Technology

    Authors

    • Robert McMichael

      National Institute of Standards and Technology

    • Sergey Dushenko

      National Institute of Standards and Technology, UMD/NIST

    • Sean M Blakley

      National Institute of Standards and Technology, UMD/NIST

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  • Machine Learning Correlates Charge Density Wave with the Local Gap in Cuprate Superconductors

    ORAL

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    Presenters

    • Kaylie Hausknecht

      Department of Physics, Harvard University

    Authors

    • Kaylie Hausknecht

      Department of Physics, Harvard University

    • Tatiana Webb

      Department of Physics, Harvard University

    • Michael Boyer

      Department of Physics, Clark University, Clark University

    • Yi Yin

      Department of Physics, Zhejiang University

    • Takeshi Kondo

      ISSP, University of Tokyo, ISSP, The Univ. of Tokyo

    • Tsunehiro Takeuchi

      Toyota Technological Institute

    • Hiroshi Ikuta

      Department of Materials Physics, Nagoya University

    • Eric Hudson

      Department of Physics, Pennsylvania State University, Penn State University, Pennsylvania State University, Physics, Pennsylvania State University

    • Jenny E. Hoffman

      Harvard University, Department of Physics, Harvard University

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  • Using Machine Learning for noise reduction in X-ray Photon Correlation Spectroscopy data to quantify time series dynamics

    ORAL

    –

    Presenters

    • Tatiana Konstantinova

      Brookhaven National Laboratory

    Authors

    • Tatiana Konstantinova

      Brookhaven National Laboratory

    • Lutz Wiegart

      Brookhaven National Laboratory

    • Anthony DeGennaro

      Brookhaven National Laboratory

    • Andi Barbour

      Brookhaven National Lab, Brookhaven National Laboratory, Brookhaven Natl Lab, National Synchrotron Light Source II, Brookhaven National Laboratory, NSLS-II, Brookhaven National Laboratory

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  • Neural network temperature predictions based on the optical properties of quantum dots

    ORAL

    –

    Presenters

    • John Colton

      Brigham Young University

    Authors

    • John Colton

      Brigham Young University

    • Charles Lewis

      Brigham Young University

    • James W Erikson

      Brigham Young University

    • Carrie E McClure

      Brigham Young University

    • Jordan Bryan

      Illinois State Univ

    • Marissa Iraca

      Lock Haven Univ, Lock Haven University

    • Derek Sanchez

      Brigham Young University

    • Greg Nordin

      Brigham Young University

    • Troy Munro

      Brigham Young University

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  • Reverse modelling for Lorentz transmission electron microscopy

    ORAL

    –

    Presenters

    • William Perry

      Department of Physics and the Quantum Theory Project, University of Florida

    Authors

    • William Perry

      Department of Physics and the Quantum Theory Project, University of Florida

    • Min He

      Institute of Physics, Chinese Academy of Sciences

    • Ying Zhang

      Institute of Physics, Chinese Academy of Sciences

    • Xiaoguang Zhang

      Department of Physics and the Quantum Theory Project, University of Florida, University of Florida, Department of Physics, Center for Molecular Magnetic Quantum Materials and Quantum Theory Project, University of Florida, Department of Physics, University of Florida, Department of Physics and the Quantum Theory Project, University of Florida, Gainesville, FL, Physics, University of Florida

    View abstract →