Academic Work

This page provides a complete list of my publications, software, talks, and posters. A detailed Curriculum Vitae is available here.

đź“š Publications

Published

  • C. Dutter, L. A. Damiano, J. Niemi, B. A. Miller. L. A. Schulte, M. Liebman, M. J. Helmers, R. M. Cruse, and M. D. McDaniel (2023). “Contour prairie strips affect adjacent soil but have only slight effects on crops”. In: Field Crops Research 296, p. 108905. DOI: 10.1016/j.fcr.2023.108905.
  • M. Nowatzke, L. Damiano, F. E. Miguez, G. S. McNunn, J. Niemi, L. A. Schulte, E. A. Heaton, and A. VanLoocke (2022). “Augmenting agroecosystem models with remote sensing data and machine learning increases overall estimates of nitrate-nitrogen leaching”. In: Environmental Research Letters 17.11, p. 114010. DOI: 10.1088/1748-9326/ac998b.
  • E. J. Ward, S. C. Anderson, L. A. Damiano, M. E. Hunsicker, and M. A. Litzow (2019). “Modeling regimes with extremes: the bayesdfa package for identifying and forecasting common trends and anomalies in multivariate time-series data”. In: The R Journal 11.2, p. 46. DOI: 10.32614/rj-2019-007.
  • L. Damiano, B. Peterson, and M. Weylandt (2018). “A Tutorial On Hidden Markov Models Using Stan”. Zenodo. DOI: 10.5281/ZENODO.1284341.

Pre-prints

  • S. Xie et al. (2025). “The Energy Exascale Earth System Model Version 3. Part I: Overview of the Atmospheric Component”. Submitted to Journal of Advances in Modeling Earth Systems. Pre-print: 10.22541/essoar.174456922.21825772/v1.
  • L. Damiano, W. M. Hannah, C.-C. Chen, J. J. Benedict, K. Sargsyan, B. Debusschere, and M. S. Eldred (2025). “Improving the quasi-biennial oscillation via a surrogate-accelerated multi-objective optimization.” Submitted to Journal of Advances in Modeling Earth Systems. arXiv: 2503.13498.
  • L. Damiano and J. Niemi (2022). “The RITAS algorithm: a constructive yield monitor data processing algorithm”. arXiv: 2209.11313.
  • L. Damiano, M. Johnson, J. Teixeira, M. D. Morris, and J. Niemi (2022). “Automatic Dynamic Relevance Determination for Gaussian process regression with high-dimensional functional inputs”. arXiv: 2209.00044.

Other Publications

  • L. Damiano and J. Niemi (2020). “Quantification of the impact of prairie strips on grain yield at the Neal Smith National Wildlife Refuge.” Iowa State University, Department of Statistics. report

đź’» Software

  • E. J. Ward, S. C. Anderson, L. A. Damiano, M. E. Hunsicker, and M. A. Litzow (2025). bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with Stan. Version 1.3.4. R package. [cran]
  • L. Damiano and J. Niemi (2022). yieldMaps: Yield Monitor Data Processing via the RITAS Algorithm. Version 0.1.0. R package. [github]
  • L. Damiano, B. Peterson, and M. Weylandt (2018). BayesHMM: Full Bayesian Inference for Hidden Markov Models. Version 0.0.1. R package. [github]

đź’¬ Talks

  • L. Damiano (2025). “Towards Realistic QBO Representation Through Surrogate-Accelerated Multi-Objective Optimization.” SIAM Conference on Computational Science and Engineering, Forth Worth, TX.
  • L. Damiano (2024). “Surrogate-based Calibration for the Quasi‑Biennial Oscillation.” Joint Statistical Meetings, Portland, OR.
  • L. Damiano (2024). “Surrogate‑Accelerated Parameter Optimization for the Quasi‑Biennial Oscillation.” SIAM Conference on Uncertainty Quantification, Trieste, Italy.
  • L. Damiano (2023). “Automatic Relevance Determination for Gaussian Processes with Functional Inputs.” Candidate seminar presented at Sandia National Laboratories, Albuquerque, NM.
  • L. Damiano (2022). “Automatic Relevance Determination for Gaussian Processes with Functional Inputs.” Candidate seminar presented at Argonne National Laboratory (MCS Mathematics and Computer Science), Virtual.
  • L. Damiano (2022). “Automatic Dynamic Relevance Determination for Gaussian process regression with functional inputs.” NASA JPL Uncertainty Quantification for Remote Sensing Inverse Problems, Virtual.
  • L. Damiano (2022). “Automatic Dynamic Relevance Determination for Gaussian process regression with functional inputs.” Pacific Northwest National Laboratory – ISU Workshop, Virtual.
  • L. Damiano (2022). “Non‑stationary log‑Gaussian Cox process for source separation in the context of the Interstellar Boundary Explorer mission.” Summer internship talk presented at Los Alamos National Laboratory, Virtual.
  • L. Damiano (2022). “Automatic Dynamic Relevance Determination for Gaussian process regressions with functional inputs.” SIAM Conference on Uncertainty Quantification, Virtual. slides
  • L. Damiano (2021). “Automatic Dynamic Relevance Determination of atmospheric states over vertical pressure grids for the MLS forward model emulation.” NASA JPL Uncertainty Quantification for Remote Sensing Inverse Problems, Virtual.
  • L. Damiano and J. Niemi (2020). “Emulation of Agricultural Production Systems sIMulator (APSIM).” Joint invited seminar at Los Alamos National Laboratory, Virtual. slides
  • L. Damiano (2020). “An autonomous algorithm for smooth yield maps.” Science‑based Trails of Rowcrops Integrated with Prairie Strips Research Symposium, Ames, IA. slides
  • L. Damiano (2019). “Augmenting Trading Systems with Hidden Markov Models using BayesHMM.” R/Finance 2019, Chicago, IL.
  • L. Damiano (2018). “Hierarchical Hidden Markov Models in High‑Frequency Stock Markets.” R/Finance 2018, Chicago, IL. slides
  • L. Damiano (2017). “Daily Stock Price Forecasts in Argentina Using Hidden Markov Models.” Inter‑American Statistical Conference 2017, Rosario, Argentina. slides
  • L. Damiano (2017). “A Quick Introduction to Hidden Markov Models Applied to Stock Volatility.” R/Finance 2017, Chicago, IL. slides; notebook

🖼️ Posters

  • L. Damiano (2024). “Surrogate‑accelerated multi‑objective optimization for improving the QBO.” SciDAC‑5 Principal Investigator Meeting, Rockville, MD.
  • L. Damiano (2023). “Automatic Relevance Determination for Gaussian Processes with Functional Inputs.” Conference on Data Analysis, Santa Fe, NM.
  • L. Damiano (2021). “Automatic Dynamic Relevance Determination of soil properties over different soil layers for yield prediction using APSIM.” Conference on Applied Statistics in Agriculture and Natural Resources, Virtual. poster
  • L. Damiano (2019). “BayesHMM: Full Bayesian Inference for Hidden Markov Models.” University of Arkansas 44th Annual Spring Lecture Series, Fayetteville, AR. poster

🏆 Honors and Awards

  • Employee Recognition Award, Sandia National Laboratories, 2025
    • Recognized for significant contributions to enhancing the mentorship experience in the CSRI Internship program, which hosts over 100 interns annually, as part of a team effort.
  • Outstanding Mentorship Award, Sandia National Laboratories, 2024
    • Recognized for providing outstanding mentorship and guidance to Sandia colleagues, modeling Sandia’s priorities while offering experiences and opportunities that prepare them for the next stage of their career.
  • Research Excellence Award, Iowa State University, 2023
    • Given to the top 10% of students writing theses or dissertations at Iowa State University.
  • Teaching Excellence Award, Iowa State University, 2023
    • Given to the top 10% of teaching assistants at Iowa State University.
  • Vince Sposito Statistical Computing Award, Department of Statistics, Iowa State University, 2022
    • For outstanding potential in statistical computing, awarded annually to one graduate student demonstrating excellence in statistical computing during their assistantship.
  • Jebe Fellowship, Department of Statistics, Iowa State University, 2018-2019
    • Awarded on admission to one graduate assistant per year.
  • Student Travel Funding, University of Arkansas 44th Annual Spring Lecture Series, 2019
  • Student Travel Award, R/Finance Conference, 2019
  • Student Travel Award, R/Finance Conference, 2018
  • Student Travel Award, R/Finance Conference, 2017