Lyndsay Shand

Observation Pathway Lead

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Observation Pathway Lead

lshand@sandia.gov

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Biography

Lyndsay has expertise in spatial and spatio-temporal statistics, point process models, Bayesian hierarchical models, and statistical methods for remotely-sensed data, atmospheric, and climate applications. For the past eight years, she has developed space-time statistical methods for observational environmental and climate data. As principal investigator of Sandia’s first Marine Cloud Brightening LDRD, her interdisciplinary team developed data-driven methods to understand the local impacts of ship emissions. Lyndsay is also a key contributor to Sandia’s Climate Security Strategy and holds an adjunct faculty position in the Department of Statistics at University of Illinois Urbana-Champaign.

Publications

  • Shand, L., Larson, K., Roesler, E., lyons, D., Gray, S., & Gray, S. (2022). An Optical Flow Approach to Tracking Ship Track Behavior Using GOES-R Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, pp. 6272-6282. https://doi.org/10.1109/JSTARS.2022.3193024 Publication ID: 80910
  • Tucker, J., Shand, L., Chowdhary, K., & Chowdhary, K. (2021). Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo. Computational Statistics and Data Analysis, 163. https://doi.org/10.1016/j.csda.2021.107298 Publication ID: 78778
  • Shand, L. (2021). Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes [Conference Presenation]. https://doi.org/10.2172/1899681 Publication ID: 76630
  • Frederick, J., Bull, D., Abbott, R.E., Shand, L., & Shand, L. (2021). Guest Lecture at the Naval Postgraduate School: The National Laboratory System & Sandia National Laboratories [Presentation]. https://www.osti.gov/biblio/1894757 Publication ID: 76459
  • Shand, L., Larson, K., Staid, A., Roesler, E., Lyons, D., Simonson, K., Patel, L., Hickey, J., Gray, S., & Gray, S. (2021). Local limits of detection for anthropogenic aerosol-cloud interactions. https://doi.org/10.2172/1855009 Publication ID: 75923
  • Shand, L. (2021). Opportunities and challenges for openly publishing statistics research for national defense applications [Conference Presenation]. https://doi.org/10.2172/1862181 Publication ID: 77930
  • Corona, E., Kramer, S., Lester, B., Jones, A.R., Sanborn, B., Shand, L., Fietek, C., & Fietek, C. (2021). Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy. https://doi.org/10.2172/1769256 Publication ID: 77228
  • Staid, A., Larson, K., Shand, L., Roesler, E., Lyons, D., & Lyons, D. (2020). Observations of ship-induced cloud tracks and methods for quantifying the duration of track visibility [Conference Poster]. https://doi.org/10.2172/1832732 Publication ID: 71912
  • Patel, L., Shand, L., Tucker, J., Huerta, J., & Huerta, J. (2020). Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database [Conference Proceeding]. https://www.osti.gov/biblio/1825584 Publication ID: 71177
  • Patel, L., Shand, L., & Shand, L. (2020). Simulating cloud-aerosol interactions made by ship emissions [Conference Proceeding]. https://www.osti.gov/biblio/1824931 Publication ID: 71141
  • Chowdhary, K., Tucker, J., Shand, L., & Shand, L. (2020). Bayesian Function Registration in R1 [Conference Poster]. https://www.osti.gov/biblio/1812166 Publication ID: 74345
  • Patel, L., Huerta, J., Shand, L., Miller, W., Tucker, J., & Tucker, J. (2020). Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database [Conference Poster]. https://www.osti.gov/biblio/1811818 Publication ID: 74294
  • Shand, L., Patel, L., & Patel, L. (2020). Simulating cloud-aerosol interactions made by ship emissions [Conference Poster]. https://www.osti.gov/biblio/1811613 Publication ID: 74253
  • Patel, L., Huerta, J., Shand, L., Miller, W., Tucker, J., & Tucker, J. (2020). Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database [Conference Poster]. https://www.osti.gov/biblio/1783652 Publication ID: 73529
  • Shand, L. (2020). Local limits of detection for anthropogenic aerosol-cloud interactions [Presentation]. https://www.osti.gov/biblio/1762578 Publication ID: 70818
  • Shand, L. (2019). Data-driven inferences for aerosol and marine low-cloud interactions using ship-based databases and open-source satellite imagery [Conference Poster]. https://www.osti.gov/biblio/1643525 Publication ID: 66619
  • Shand, L. (2019). Sandia Overview [Presentation]. https://www.osti.gov/biblio/1646427 Publication ID: 66497
  • Shand, L., Knepper, R., Bolintineanu, D., Wilson, L., Kittell, D.E., & Kittell, D.E. (2019). Nonstationary Spatial Methods for Microstructure Reconstruction [Conference Poster]. https://www.osti.gov/biblio/1641429 Publication ID: 70175
  • Shand, L. (2019). Data-driven Approach to Cloud-Aerosol Interactions [Presentation]. https://www.osti.gov/biblio/1645367 Publication ID: 68889
  • Tucker, J., Harris, T., Li, B., Shand, L., & Shand, L. (2019). Identifying Outliers in Functional Data with Elastic Depth [Presentation]. https://www.osti.gov/biblio/1644855 Publication ID: 67957
  • Shand, L., Staid, A., Roesler, E., & Roesler, E. (2019). Local limits of detection for anthropogenic aerosol-cloud interactions [Presentation]. https://www.osti.gov/biblio/1593214 Publication ID: 64350
  • Tucker, J., Shand, L., & Shand, L. (2018). Identifying Phase and Amplitude Extremes in Functional Data with Elastic Depth [Presentation]. https://www.osti.gov/biblio/1561429 Publication ID: 64099
  • Shand, L. (2018). Spatially varying auto-regressive models for prediction of new HIV diagnoses [Conference Poster]. https://www.osti.gov/biblio/1569194 Publication ID: 63009
  • Tucker, J., Lewis, J.R., Shand, L., Lane, J., & Lane, J. (2018). Bayesian Modeling of Self-Exciting Marked Point Processes with Missing Histories [Conference Poster]. https://www.osti.gov/biblio/1527084 Publication ID: 62604
  • Lewis, J.R., Tucker, J., Shand, L., & Shand, L. (2018). Bayesian Modeling of Self-Exciting Marked Point Processes with Missing Histories [Presentation]. https://www.osti.gov/biblio/1499221 Publication ID: 60971
  • Tucker, J., Lewis, J.R., Shand, L., Lane, J., & Lane, J. (2018). Bayesian Modeling of Self-Exciting Marked Point Processes with Missing Histories [Conference Poster]. https://www.osti.gov/biblio/1498224 Publication ID: 60863
  • Guo, S., Cooper, M.A., Shand, L., & Shand, L. (2017). A Statistical Representation of Pyrotechnic Research Igniter Output [Conference Poster]. https://doi.org/10.1063/1.5044973 Publication ID: 58367
  • Guo, S., Cooper, M.A., Shand, L., & Shand, L. (2017). A Statistical Representation of Pyrotechnic Igniter Output [Conference Poster]. https://doi.org/10.1063/1.5044973 Publication ID: 57444
  • Guo, S., Cooper, M.A., Shand, L., & Shand, L. (2017). A Statistical Representation of Pyrotechnic Igniter Output [Conference Poster]. https://doi.org/10.1063/1.5044973 Publication ID: 57720
Showing 10 of 29 publications.