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  • Lisa Moore

  • Scientist, CCS-6
  • Email
  • (505) 844-1302

Dr. Leslie M. (Lisa) Moore joined the Statistical Sciences Group at Los Alamos National Laboratory in 1985 and has been a Guest Scientist since 2012. Dr. Moore has extensive experience as a professional statistician working with individual scientists and engineers as well as with medium to large multidisciplinary teams at LANL. She has brought statistical thinking, rigor and methods to a wide variety of problems of scientific and national importance including critical infrastructure protection, transportation simulation, carbon capture implementation, nuclear plant reliability, science-based stockpile stewardship (i.e., LANL's national security mission to ensure the safety and reliability of the US nuclear deterrent by developing and applying science and technology in the absence of nuclear testing), supercomputer performance and environmental impact. She has worked with scientists in many different disciplines including chemists, material scientists, engineers, physicists, epidemiologists and ecologists to name a few.

Dr. Moore’s primary area of expertise is design of experiments for physical and simulation-based studies and analysis of designed experiments in support of study objectives. As an experimental design expert, her role is to help with problem definition and experiment planning as well as analysis. In physical experiments, her research addresses challenges posed by limits on resources and experimental capability. In simulation-based experiments, her research focuses on design of efficient simulation ensemble experiments for a variety of analysis objectives but with focus on sensitivity analysis as a driving aspect of uncertainty quantification in models and analysis.

Dr. Moore’s areas of expertise include:

  • Statistical Theory and Methods for Experimentation
  • Design of Experiments: Fractional Factorial Design
  • Design of Computer Experiments: Latin Hypercube Sampling, Orthogonal Array and Projection Array based LHS
  • Statistical Learning
  • General and Generalized Linear Models
  • Collaborative Statistical Consulting and Research