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Section on Physical and Engineering Sciences News for April 2018

1 April 2018 1,195 views No Comment
Joanne Wendelberger, Joint Research Conference Chair

    Make your plans now to head to Santa Fe, New Mexico, for the 2018 Joint Research Conference (JRC) on Statistics in Industry and Technology, which will be hosted by the Los Alamos National Laboratory at the Drury Plaza Hotel June 11–14. JRC2018 is a joint meeting of the SPES/Institute of Mathematical Statistics Spring Research Conference on Statistics in Industry and Technology and the Quality and Productivity Section’s Quality and Productivity Research Conference.

    A short course titled “Bridging Statistics and Data Science” will be taught by Ming Li from Amazon and Hui Lin from DowDuPont. Conference activities include a tour and reception at the Los Alamos National Laboratory Bradbury Science Museum. There will also be an opportunity to experience the Meow Wolf interactive exploration of art and technology.

    The conference program committee, co-chaired by Xinwei Deng and Brian Weaver, has arranged a stellar lineup of invited sessions. Plenary speakers include this year’s conference honoree, Max Morris from Iowa State University; Scott Vander Wiel from Los Alamos National Laboratory; and Derek Bingham from Simon Fraser University. Special invited luncheon speakers include ASA President-elect Karen Kafadar of the University of Virginia, who will give a talk titled “The Critical Role of Statistics in Development and Validation of Forensic Methods,” and Francesca Samse of The University of Texas at Austin, who will discuss work color perception and scientific visualization.

    Invited Sessions

    The Technometrics invited session will feature Mickael Binois with “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments,” Joseph Guinness with “Permutation and Grouping Methods for Sharpening Gaussian Process Approximations,” and Matthias Tan with “Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations.”

    The Journal of Quality Technology (JQT) invited session will include Doug Montgomery, who will speak about 50 years of JQT; Michael Hamada, who will discuss estimation of a service-life distribution based on production counts and a failure database; and John R. Lewis, who will talk about selecting an informative/discriminating multivariate response for inverse prediction.

    Lessons Learned from Data Challenges and Challenging Data

    • Anne Hansen, “Overcoming Data Obstacles and Driving a Data Culture”
    • David Osthus, “When Flu Forecasting Isn’t About the Flu: What I’ve Learned Participating in the CDC’s Influenza Forecasting Challenge”
    • Christine Anderson-Cook, “Data Competition Hosting: Getting More Than Just a Winner Through Strategic Design and Analysis”

    Data Science in New Mexico

    • Lauren Hund, “Strategies for Calibrating Inexact Computer Models to Estimate Physical Parameters”
    • Oleg Makhnin, “gibbSeq: A Bayesian Multiple Testing Method for Genetics Applications”
    • James Degnan, “Using Approximate Bayesian Computation to Infer Evolutionary Trees”

    Test Planning for Reliability

    • Laura Freeman, “Challenges and New Methods for Designing Reliability Experiments”
    • Lu Lu, “New Developments on Demonstration Test Plans”
    • Isaac Michaud, “Using Mutual Information for Designing Sensitivity Tests”

    Astrostatistics Interest Group

    • Luis Campos, “Disentangling Astronomical Sources with Spatial, Spectral, and Temporal X-Ray Data”
    • Gwendolyn Eadie, “Estimating the Mass to Light Ratio of the Milky Way’s Nuclear Star Cluster and Its Central Black Hole”

    Design for Computer Experiments

    • Robert Gramacy, “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments”
    • Matthew Plumlee, “Calibration with Frequentists Coverage and Consistency”
    • Daniel W. Apley, “Input Mapping for Calibration of High/Low Fidelity Simulation Models with Mismatched Inputs”

    Design for Physical Experiments

    • Jeff Wu, “Analysis of Marginal Tail Means: A Robust Method for Parameter Design Optimization”
    • Xun Huan, “Value of Feedback and Forward-Looking in Bayesian Sequential Optimal Experimental Design”
    • Ryan Lekivetz, “Restricted Screening Designs”

    Statistical Machine Learning

    • Tom Loughin, “Adaptively Pruned Random Forests for Modeling Means and Variances Simultaneously”
    • Nicholas Henderson
    • Rob McCulloch

    Uncertainty Quantification

    • Michael Grosskopf
    • Peter Marcy, “Bayesian Gaussian Process Models for Dimension Reduction Uncertainties”
    • Jared Huling, “Neural Networks for Flexible and Fast Emulation of Computer Experiments”

    Invited sessions on statistical process control, physics applications, and functional data are also being planned.

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