Luca Sartore

NISS Position: 
Research Fellow II -- Statistician

Luca Sartore is a Research Fellow II - Statistician for NISS, working with the National Agricultural Statistical Service (NASS). He received his Bachelor of Science degree statistics and computer science for management and master degree in statistics for business from the Ca' Foscari University of Venice. He got his Ph.D. from University of Padua in statistical sciences. Prior to NISS, he was a research fellow for the European Centre for Living Technology.

Research Interests

My research interests encompass a range of topics within the field of statistics. I am particularly focused on high-performance computational statistics, spatiotemporal statistics (including estimation, prediction, and uncertainty quantification), regression methods, multivariate statistics, nonparametric and semiparametric statistics, which incorporate machine learning and artificial intelligence techniques. I also have experience in and proficiency with outlier detection, analysis of anomalous data, nonresponse adjustments, calibration weighting techniques (including methodologies for surveys and censuses), data privacy, and disclosure control procedures. I am proficient in modeling complex systems and signal processing.

Published Software

  • Historical, Relational, and Tail Anomaly-Detection Algorithms (HRTnomaly). An R package for detecting anomalous data entries on repeated survey. Published: 20.February.2025. Available at: https://cran.r-project.org/package=HRTnomaly
  • Prediction Limits for the Poisson Distribution (plpoisson). An R package for computing the predictive limits of a Poisson distribution. Published: 04.June.2020. Available at: https://cran.r-project.org/package=plpoisson
  • Automatic Differentiation with Dual Numbers (dual). An R package for the automatic computation of first derivatives. Published: 18.December.2019. Available at: https://cran.r-project.org/package=dual
  • Integer calibration (inca). An R package for control rounding and integer adjustments of survey or census weights. Published: 03.August.2016. Available at: https://cran.r-project.org/package=inca
  • Continuous lag spatial Markov chain (spMC). An R package for analysis of categorical spatial data. Published: 19.March.2012. Available at: https://cran.r-project.org/package=spMC

Technical Strengths

Operative systems: Linux, Unix, Windows, OSX

Statistical software: R, Maxima, Julia, MATLAB/Octave, SAS, Mathematica, STATA

Programming: C, Python, Bash, C++, Qt, JavaScripts, Visual Basic .Net, VBScripts, Zig, Java, Fortran, GNU and Intel x64 Assembly, Pascal

Markup Languages: LaTeX, Markdown, Quarto, XML, HTML, CSS, ASP, reStructuredText

Information Systems: Google Earth Engine, QGIS, ArcGIS, SQL, MySQL, Oracle Databases

Other computer software: LibreOffice, MS Office, Git, ERDAS Imagine, Paraview

Other skills:

    • Cloud computing (Google Cloud, Amazon AWS, Microsoft Azure, and Databricks)
    • Parallel computing (OpenMP, SIMD, SLURM, and CUDA)
    • Quantum computing

Education

University of Padua, Italy
Ph.D. Statistics - Department of Statistics
Scholarship from the Università di Padova for PhD graduate studies from 2011-2013.
Research topic: Quantile Regression in Non-Linear Models for Rainfall Data
Supervisor: Prof. Renato Guseo. Co-supervisor: Dr. Claudia Furlan.

University Ca’ Foscari of Venice, Italy
M.S. Statistics for Business
Research topic: Geostatistical Models for 3-D Data.
Supervisor: Prof. Carlo Gaetan
Grade: 110/110 with honors

University Ca’ Foscari of Venice, Italy
B.S. Statistics and Computer Science for Management
Research topic: Composite Likelihood Estimation for Geostatistical Data.
Supervisor: Prof. Carlo Gaetan
Grade: 108/110.

Work Experience

National Institute of Statistical Sciences (NISS)

July 2019 - Current in Washington DC, USA
Working experience as a Senior Researcher

  • Statistical consultancy at the United States Department of Agriculture (USDA) for the National Agriculture Statistics Service (NASS).
  • Developed algorithms to predict crop yield at the county level via innovative uses of remotely sensed data.
  • Actively assisted to the conversion of production software from Pascal code to R.
  • Supported the adoption of the integer calibration for the Farm Labor Survey.
  • Developed a rounding algorithm for official statistics to be consistent with administrative data constraints.
  • Developed algorithms to predict next crop rotation patterns and their uncertainty for the June-Area-Survey manual-imputation process.
  • Mentored national and international Ph.D. students and early-career statisticians at SDSS 2022.
  • Developed the likelihood for the dual-system estimation of the record weights for the Census of Agriculture.
  • Developed resampling algorithms to estimate the variances of Census totals and medians.
  • Developed algorithms to estimate planted acreage at the state level by linking survey data and remotely sensed images.
  • Developed a cloud-based solution for detecting flooded areas and quantifying damaged crop areas
  • Developed frequentist and Bayesian outlier detection algorithms to identify and correct cellwise anomalies in NASS datasets.
  • Developed machine learning algorithm to synthesize data to protect privacy of survey/census respondents.
  • Developed a multivariate approach to evaluate empirical cumulative distribution functions.
  • Developed and tested the utility of a Bayesian method using Gaussian copula and pseudo-likelihoods to privatize multidimensional mixed-type datasets.
  • Served as a USDA Judge for the Harvard Data Science Initiative at the Agri-Datathon (https://datascience.harvard.edu/2024/10/25/).

National Institute of Statistical Sciences

May 2015 - June 2019 in Washington DC, USA
Working experience as a Research Associate

  • Statistical consultancy at USDA for NASS.
  • Developed integer calibration weighting methodology for the 2017 US Census of Agriculture.
  • Improved the variance estimation of the quantities published for the 2017 US Census of Agriculture.
  • Researched alternative techniques to the current dual-system estimation used for the US Census of Agriculture.
  • Investigated relationships among rounding rules applied to official statistics prior to publication.
  • Developed of a new dynamical model to improve the estimation of US swine inventories.
  • Contributed to the National Academy of Sciences, Engineering, and Medicine proceeding report “Using models to estimate hog and pig inventories” ISBN-13: 978-0-309-49572-1.

European Centre for Living Technologies at the University Ca’ Foscari of Venice

March 2014 - March 2015 in Venezia, Italy
Working experience as a Research Assistant

  • Reviewed the literature related to the heating ventilation air-conditioned systems.
  • Developed statistical models for the reduction of energy consumption by improving thermal management.

Pegaso Informatica S.R.L.

August 2007- October 2007 in Fontaniva, Italy
Working experience as a Software Programmer

  • Developed interfaces for accounting software and wrote technical manuals.
  • Provided technical assistance to costumers.
  • Maintained part of the company website and intranet interface.

Awards and Scholarships

  1. First-quarter award for significant contributions to the Conservation Data Team’s Cover Crop and Evaluation of Statistical Properties Sub-teams (Research and Development Division of USDA-NASS).
  2. Best poster award for Jacobsen, et al. (2024) at the Privacy and Public Policy Conference.
  3. United States Department of Agriculture Secretary’s Award for outstanding research, development and implementation of the Predictive Cropland Data Layers and an intensive cloud computing environment (Integrated Modeling and Geospatial Estimation System (IMAGES): Modeling Team of USDA-NASS).
  4. Research, Education, and Economics (REE) Under Secretary’s Award for outstanding research, development and implementation of the Predictive Cropland Data Layers, early-season crop acreage modeled estimates, Crop Sequence Boundaries, and establishment of an intensive cloud computing environment (Integrated Modeling and Geospatial Estimation System (IMAGES): Modeling Team of USDA-NASS).
  5. Second-quarter award for the contributions to the development of the new Census estimation methodology (Research and Development Division of USDA-NASS).
  6. First place award. “For developing a cell-wise detection algorithm at the NAGGLE competition” (USDA-NASS).
  7. Circle award. “For producing timely and useful data for the editing and imputation stages of the 2021 June Area Survey by imagining and building the June Area Land Tool” (USDA-NASS).
  8. Circle award. “For tremendous efforts in implementing new and high-quality Labor Survey statistics in service to the U.S. agriculture” (USDA-NASS).
  9. Circle award. “For terrific teamwork and technical expertise in implementing new Cash Rent statistics with an updated outlier identification process and a new modeling process” (USDA-NASS).
  10. Third-quarter award for developing the Predictive Cropland Data Layer (Research and Development Division of USDA-NASS).
  11. Second-quarter award for software conversion of the primary sampling technology (Research and Development Division of USDA-NASS).
  12. Circle award. “For implementing new Farm Labor statistics requested by the Secretary of Agriculture” (USDA-NASS).
  13. Collaborator of the year(USDA-NASS Administrator’s Award).
  14. Spot award for early acreage estimation (Research and Development Division of USDA-NASS).
  15. Spot award for labor survey calibration (Research and Development Division of USDA-NASS).
  16. Spot award for swine modelling (Research and Development Division of USDA-NASS).
  17. Spot award for excellence in research and collaboration efforts within and across divisions, and with Field Offices on an ongoing research and review of modeled county estimates for corn, soy, winter wheat, and upland cotton (Research and Development Division of USDA-NASS).
  18. Spot award for integer calibration (Research and Development Division of USDA-NASS).
  19. PhD scholarship(University of Padua).

Peer-Reviewed Publications

Sartore, L., Boryan, C., Rosales, A., Nagle, A. (2024) Crop Prediction Uncertainty Maps. In IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, 4735-4739. IEEE. Available at: https://doi.org/10.1109/IGARSS53475.2024.10642026

Sartore, L., Chen, L., Bejleri, V. (2024) Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers. Stats, 7(4), 1244-1258. Available at: https://tinyurl.com/stats7040073

Sartore, L., Chen, L., van Wart, J., Dau, A., Bejleri, V. (2024) Identifying Anomalous Data Entries in Repeated Surveys. Journal of Data Science. 22(3), 436–455. Available at: https://doi.org/10.6339/24-JDS1136

Corral, G., Sartore, L., Vande Pol, K., Abreu, D., Young, L.J. (2024) Using Machine Learning Algorithms to Identify Farms on the 2022 Census of Agriculture. Statistical Journal of the International Association for Official Statistics. Available at: https://doi.org/10.3233/SJI-230089

Nagle, A., Boryan, C., Dau, A., Rosales, A., Sartore, L., Willis, P. (2024) Utilizing Remote Sensing and Geospatial Data to Characterize Administrative Data Undercoverage for Crop Acreage Estimation. In IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, 9979-9982. IEEE. Available at: https://doi.org/10.1109/IGARSS53475.2024.10641401

Sartore, L., Boryan, C., Chen, L. (2023) Utilizing Land Cover, Satellite and Agricultural Survey Data to Produce Early Season Crop Acreage Estimates. IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium 3434-3437, Available at: https://doi.org/10.1109/IGARSS52108.2023.10282795

Sartore, L., Boryan, C., Dau, A., Willis, P. (2023) An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks. Journal of Data Science. Available at: https://doi.org/10.6339/23-JDS1098

Abernethy, J., Beeson, P., Boryan, C., Hunt, K., Sartore, L. (2023) Preseason crop type prediction using crop sequence boundaries. Computers and Electronics in Agriculture, 208, 107768. Available at: https://doi.org/10.1016/j.compag.2023.107768

Sartore, L., Boryan, C., Willis, P. (2022) Developing entropies of Predictive Cropland Data Layers for crop survey imputation. IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 1404–1407, Available at: https://doi.org/10.1109/IGARSS46834.2022.9884059

Sartore, L., Rosales, A.N., Johnson, D.M., and Spiegelman, C.H. (2022) Assessing Machine Leaning Algorithms on Crop Yield Forecasts Using Functional Covariates Derived from Remotely Sensed Data. Computers and Electronics in Agriculture, 194, 106704. Available at https://doi.org/10.1016/j.compag.2022.106704

Bejleri, V., Sartore, L. and Nandram, B. (2022) Asymptotic equivalence between frequentist and Bayesian prediction limits for the Poisson distribution. Journal of the Korean Statistical Society, 51(3), 633–665, Springer Nature Singapore. Available at: https://doi.org/10.1007/s42952-021-00157-x

Chen, L., Sartore, L., Benecha, H., Bejleri, V., Nandram, B. (2022) Smoothing county-level sampling variances to improve small area models’ outputs. Stats, 5(3), 898–915, MDPI. Available at: https://doi.org/10.3390/stats5030052

Hyman, M., Sartore, L. and Young, L.J. (2022) Capture-recapture estimation of characteristics of US local food farms using a web-scraped list frame. Journal of Survey Statistics and Methodology, 10(4), 979–1004, Oxford University Press. Available at: https://doi.org/10.1093/jssam/smab008

Sartore, L., Wei, Y., Abayomi, E., Riggins, E., Corral, G., Bejleri, V., Spiegelman, C. (2020) Modeling swine population dynamics at a finer temporal resolution. Applied Stochastic Models in Business and Industry, 36(6), 1060–1079. Available at: https://doi.org/10.1002/asmb.2597

Fabbri, P., Gaetan, C., Sartore, L., Dalla Libera, N. (2020) Subsoil reconstruction in geostatistics beyond kriging: a case study in Veneto (NE, Italy). Hydrology, 7(1), 15. Available at: https://www.mdpi.com/2306-5338/7/1/15/pdf

Sartore, L., Toppin, K., Young, L., Spiegelman, C. (2019) Developing integer calibration weights for the Census of Agriculture. Journal of Agricultural, Biological, and Environmental Statistics, 24(1), 26–48. Available at: https://doi.org/10.1007/s13253-018-00340-4

Yazdani, A., Yazdani, A., Giráldez, R. M., Aguilar, D., Sartore, L. (2019) A multi-trait approach identified genetic variants including a rare mutation in RGS3 with impact on abnormalities of cardiac structure/function. Scientific Reports, 9(1), 5845. (ISSN 2045-2322). Available at: https://doi.org/10.1038/s41598-019-41362-3

Sartore, L., Fabbri, P., Gaetan, C. (2016) spMC: an R-package for 3D lithological reconstructions based on spatial Markov chains. Computers & Geosciences, 94, 40–47. Available at: https://doi.org/10.1016/j.cageo.2016.06.001

De March, D., Borrotti, M., Slanzi, D., Sartore, L., Podestà, L., Poli, I. (2015) A predictive approach based on neural network models for building automation systems, Advances in Neural Networks: Computational and Theoretical Issues in SMART INNOVATION, SYSTEMS AND TECHNOLOGIES, Springer International Publishing, vol. 37, pp. 253–262 (ISBN 978-3-319-18163-9) (ISSN 2190-3018)
Available at: https://doi.org/10.1007/978-3-319-18164-6_24

Sartore, L. (2013) spMC: modelling spatial random fields with continuous lag Markov chains. The R Journal, 5(2), 16–28. Available at: https://journal.r-project.org/archive/2013-2/sartore.pdf

Publications in Conference Proceedings

Emmet, R.L., Benecha, H., Sartore, L., Craig, B.A. (2024) Developing a Triple-System Estimator with List Dependence and Nonresponse. In Proceedings of the Survey Research Methods Section, JSM 2024. Available at https://doi.org/10.5281/zenodo.13982375

Abernethy, J., Boryan, C., Guindin, N., Hunt, K., Sartore, L. (2023) Early season corn acreage estimates in the presence of extreme weather. In Proceedings of the Survey Research Methods Section, JSM 2023. Available at https://doi.org/10.5281/zenodo.8377224

Rhodes, S.O., Rosales, A., Sartore, L., Murphy, T. (2023) Uncertainty assessment for the imputation of an area survey. In Proceedings of the Survey Research Methods Section, JSM 2023. Available at https://doi.org/10.5281/zenodo.8383528

Sartore, L., Benecha, H., Bejleri, V. and Chen, L. (2022) Uncertainty Assessment of Finite-population Medians Under Complex Capture-Recapture Sampling Designs. In Proceedings of the Survey Research Methods Section, JSM 2022. Available at http://www.asasrms.org/Proceedings/y2022/files/322422_100102.pdf

Abernethy, J., Sartore, L., Yoon, G., Craig, B.A., Abreu, D.A., Young, L.J. (2022) A computationally efficient model for large scale crop type forecasting. In Proceedings of the Survey Research Methods Section, JSM 2022.

Benecha, H., Sartore, L., Yoon, G., Craig, B.A., Abreu, D.A., Young. L.J. (2022) Extending the Dual-system Estimation for the Census of Agriculture. In Proceedings of the Survey Research Methods Section, JSM 2022. Available at http://www.asasrms.org/Proceedings/y2022/files/322951_161688.pdf

Murphy, T., Rosales, A., Sartore, L., Abreu, D.A. (2022) Automatic imputation for an area survey. In Proceedings of the Survey Research Methods Section, JSM 2022. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2022/JSM_JASautoimputation_final.pdf

Benecha, H., Sartore, L., Cruze, N. (2019) Model-based crop yield forecasting: covariate selection and related issues. In Proceedings of the Survey Research Methods Section, JSM 2019. http://www.asasrms.org/Proceedings/y2019/files/1199472.pdf

Corral, G., Riggins, S., Abayomi, E., Sartore, L., Wei, Y., Sedransk, N., Spiegelman, C., Young, L.J. (2019) On producing estimates for NASS’s quarterly hogs and pigs report.
Available at: https://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_192694.pdf

Toppin, K., Sartore, L., Spiegelman, C. (2018) Toward a global convergent algorithm for integer calibration weighting. In Proceedings of the Survey Research Methods Section, JSM 2018. Available at: http://www.asasrms.org/Proceedings/y2018/files/867092.pdf

Wei, Y. Sartore, L., Abernethy, J., Miller, D., Toppin, K., Spiegelman, C. (2018) Deep learning for data imputation and calibration weighting. In Proceedings of the Survey Research Methods Section, JSM 2018. Available at: http://www.asasrms.org/Proceedings/y2018/files/867093.pdf

Sartore, L., Benecha, H., Toppin, K., Spiegelman, C. (2017) Restricted multinomial regression for a triple-system estimation with list dependence. In Proceedings of the Government Statistics Section, JSM 2017.

Sartore, L., Toppin, K., Spiegelman, C. (2017) Estimated covariance matrices associated with calibration. In Proceedings of the JSM 2017.

Abernethy, J., Sartore, L., Benecha, H., Spiegelman, C. (2017) Estimation of capture probabilities by accounting for sample designs. In Proceedings of the Government Statistics Section, JSM 2017.

Benecha, H., Abreu, D.A., Abernethy, J., Sartore, L., Young, L.J. (2017) Evaluation of a new approach for estimating the member of US farms. In Proceedings of the Government Statistics Section, JSM 2017. Available at: http://www.asasrms.org/Proceedings/y2017/files/593986.pdf

Toppin, K., Sartore, L., Spiegelman, C. (2017) Design weights and calibration. In Proceedings of the Government Statistics Section, JSM 2017.

Sartore, L., Toppin, K., Spiegelman, C. (2016) Introducing a new calibration procedure. USDA NASS Research Report RDD-a6-STS. Washington, DC. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/New_Integer_Calibration_%20Procedure_2016.pdf

Borrotti, M., Sartore, L., Slanzi, D. (2014) Intelligent control system model-based optimisation for energy saving. Proceeding of ERCIM 2014. ISBN 9788493782245. Available at: https://iris.unive.it/retrieve/e4239ddb-7137-7180-e053-3705fe0a3322/ERCIM2014.pdf

Invited Seminars

Sartore, L. (2024) Crop prediction via neural network models. At the Hohenheim University in Stuttgart, BW. Germany.

Sartore, L. (2024) Identifying anomalous data entries in repeated surveys. At the American University in Washington, DC. USA.

Sartore, L., Abernethy, J., Boryan, C., Chen, L., Hunt, K., Spiegelman, C., Young, L. J. (2020) Empirical data-fusion approaches to generate model covariates. Webinar at the Federal Committee on Statistical Methodology (FCSM) Geospatial Interest Group. Washington, DC. USA.

Sartore, L. (2017) Semiparametric quantile-sheets estimation. At the Worcester Polytechnic Institute in Worcester, MA. USA.

Sartore, L. (2014) Spatiotemporal Markov chains. At the United States Department of Agriculture’s National Agricultural Statistics Service in Washington DC. USA.

Conference Contributions

Sartore, L., Boryan, C. (2024) Predictive Cropland Data Layer and Uncertainty Measures (SDSS) in Richmond, VA. USA.

Sartore, L., Boryan, C., Rosales, A., Nagle, A. (2024) Crop Prediction Uncertainty Maps (IGARSS) in Athens. Greece.

Sartore, L., Chen, L., Bejleri, V. (2024) Efficient Evaluation of Multivariate Empirical Cumulative Distribution Functions (SDSS) in Richmond, VA. USA.

Sartore, L., Chen, L., Jacobsen, M., Yeo, K., Bejleri, V. (2024) High-dimensional synthetic data via nearest neighbors (PPPC) in Washington, DC. USA.

Sartore, L., Chen, L., van Wart, J., Dau, A., Bejleri, V. (2024) Identifying Anomalous Data Entries in Repeated Surveys (JSM) in Portland, OR. USA.

Sartore, L., Emmet, R.L., Benecha H., Craig, B.A. (2024) Capture-Recapture in the Age of AI (CNSTAT AI Day 2024) in Washington, DC. USA.

Sartore, L., Siegfried, Y., Bejleri, V. (2024) Improvements to Deep Isolation Forests for Identifying Anomalous Records (AIUF) in College Station, TX. USA.

Benecha, H., Cheng, Y., Jacobsen, M., Grant, J., Chen, L., Sartore, L., Bejleri, V. (2024) Exploring the Application of Differential Privacy to a Subset of the Cells of a Table (FCSM) in College Park, MD. USA.

Bleher, J., Sartore, L. (2024) Fast Non-Parametric Test on the Equivalence of Multivariate Empirical Distributions (CMSTAT) in London. United Kingdom.

Chen, L., Sartore, L., Jacobsen, M., Yeo, K., Bejleri, V. (2024) Bayesian data synthesis for data with weights (PPPC) in Washington, DC. USA.

Corral, G., Sartore, L., Vande Pol, K., Abreu, D., Young, L.J. (2024) Using machine learning algorithms to identify farms on the 2022 Census of Agriculture (CNSTAT AI Day 2024) in Washington, DC. USA.

Emmet, R.L., Benecha, H., Sartore, L., Craig, B.A. (2024) Capture-Recapture in the Age of AI (FCSM) in College Park, MD. USA.

Emmet, R.L., Benecha, H., Sartore, L., Craig, B.A. (2024) Developing a Triple-System Estimator with List Dependence and Nonresponse (JSM) in Portland, OR. USA.

Hogland, J., Liu, G., Lu, R., Sartore, L., Woodward-Greene, J. (2024) Moderated panel: Practical applications of AI across the USDA research portfolio (AIUF) in College Station, TX. USA.

Jacobsen, M., Yeo, K., Sartore, L., Chen, L. (2024) Synthetic microdata methods for confidential agricultural data (PPPC) in Washington, DC. USA.

Mulherin, S., Nicholson, W., Maley, K.L.D., Chuang-Stein, C., Sartore, L., Yu, C. (2024) Statistical Careers in Industry and Government (NISS Graduate Student Network Research Conference 2024) Virtual conference. USA.

Nagle, A., Boryan, C., Dau, A., Rosales, A., Sartore, L., Willis, P. (2024) Utilizing remote sensing and geospatial data to characterize administrative data undercoverage for crop acreage estimation (IGARSS) in Athens. Greece.

Rhodes, S., Sartore, L., Murphy, T., Rosales, A. (2024) Using Neural Networks to Assess Uncertainty for the Imputation of an Area Survey (CNSTAT AI Day 2024) in Washington, DC. USA.

Rosales, A., Murphy, T., Sartore, L., Abernethy, J., Emmet, R.L. (2024) Automatic Imputation in an Agricultural Area Frame Survey (International Conference on Establishment Statistics VII) in Glasgow, GB-SCT. UK.

Rosales, A., Murphy, T., Sartore, L., Abernethy, J., Emmet, R.L., Young, L.J. (2024) Leveraging Geospatial Data and Machine Learning for Machine Imputation in an Annual Area Frame Survey (JSM) in Portland, OR. USA.

Savitsky, T., Sartore, L., Chen, L. (2024) A Privatized Bayesian Hierarchical Copula Framework for the Synthetic Multivariate Mixed-Type Data Generation. Poster to be presented at ISBA in Venice, Italy.

Wasserstein, R., Erciulescu, A., Klopper, J., Freeman, L., Sartore, L., Oh, D., Young, L.J. (2024) Opportunities and Challenges for Statisticians in the Era of AI (Georges’ STAT DAY 2024) in Washington, DC. USA.

Sartore, L., Chen, L. (2023) Consistency of survey estimates through adjusted integer weights (JSM) in Toronto, ON. Canada.

Sartore, L., Chen, L., Boryan, C. (2023) Utilizing land cover, satellite and agricultural survey data to produce early season crop acreage estimates (IGARSS) in Pasadena, CA. USA.

Sartore, L., Chen, L., Van Wart, J., Dau, A., Bejleri, V. (2023) Fuzzy detection of cellwise anomalies in agricultural surveys (ICAS) in Washington, DC. USA.

Sartore, L., Chen, L., Van Wart, J., Dau, A., Bejleri, V. (2023) Identification of anomalous data entries in repeated surveys (GASP) Virtual conference. USA.

Abernethy, J., Guindin, N., Hunt, K., Boryan, C., Sartore, L. (2023) Early season corn acreage stimates in presence of extreme weather (JSM) in Toronto, ON. Canada.

Chen, L., Sartore, L., Abernethy, J., Young, L.J. (2023) Integration of survey and non-survey data in the eestimation of planted acreage (ICAS) in Washington, DC. USA.

Rhodes, S., Rosales, A., Sartore, L., Murphy, T. (2023) Optimizing Imputation for an Area Survey (JSM) in Toronto, ON. Canada.

Sartore, L., Benecha, H., Bejleri, V., Chen, L. (2022) Uncertainty assessment of finite-population medians under complex sampling designs (JSM) in Washington, DC. USA.

Sartore, L., Boryan, C., Dau, A., Willis, P. (2022) Predicting crop-specific land-cover using transition probabilities, deep and quantum-inspired neural network models (SDSS) in Pittsburgh, PA. USA.

Sartore, L., Boryan, C., Willis, P. (2022) Developing entropies of predictive cropland data layers for crop survey imputation (IGARSS) Hybrid conference based in Kuala Lumpur. Malaysia.

Abernethy, J., Sartore, L., Hunt, K., Boryan, C. (2022) A computationally efficient model for large scale crop type forecasting (JSM) in Washington, DC. USA.

Benecha, H., Sartore, L., Yoon, G., Craig, B. A., Abreu, D. A., Young, L. J. (2022) Extending the dual-system estimation for the Census of Agriculture (JSM) in Washington, DC. USA.

Murphy, T., Rosales, A., Sartore, L., Abreu, D. A. (2022) Automatic imputation for an area survey (JSM) in Washington, DC. USA. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2022/JSM2022_Murphy_final.pdf

Sartore, L., Benecha, H. (2021) Estimation of power transformations in capture-recapture models (JSM) Virtual conference. USA.

Sartore, L., Abernethy, J., Boryan, C., Chen, L., Hunt, K., Spiegelman, C., Young, L. J. (2020) Empirical data-fusion approaches to generate model covariates (JSM) Virtual conference. USA.

Sartore, L., Boryan, C. (2020) Crop classification and uncertainty assessment at 10-m resolution using Google Earth Engine (GASP) Virtual conference. USA. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/GASP-2020/Crop%20classication%20and%20uncertainty%20assessment%20at%2010m%20resolution%20using%20Google%20Earth%20Engine.pdf

Sartore, L., Rosales, A., Johnson, D., Spiegelman, C. (2020) Linking official statistics and remote sensing data for training crop yield regression models (FCSM) Webinar on “Blended Data”.

Abernethy, J., Boryan, C., Hunt, K., Sartore, L. (2020) Early season planted acreage estimates using machine learning (JSM) Virtual conference. USA.

Toppin, K., Sartore, L. (2020) Classifying evolving data streams (JSM) Virtual conference. USA.

Sartore, L., Rosales, A., Johnson, D., Dorn, M. F., Spiegelman, C. (2019) A machine-learning approach to extract remote-sensing features for predicting crop yield (JSM) in Denver, CO. USA.

Sartore, L., Rosales, A., Johnson, D., Dorn, M. F., Spiegelman, C. (2019) Synergy between remote sensing and machine learning for crop yield prediction (GASP) in Washington, DC. USA. Available at: http://washstat.org/materials.html/20190923/Sartore.pdf

Sartore, L., Rosales, A., Johnson, D., Spiegelman, C. (2019) Predicting crop yield using spatio-temporal functional covariates (UMD) in College Park, MD. USA.

Sartore, L., Wei, Y., Abayomi, E., Riggins, S., Corral, G., Sedransk, N. (2019) New developments in modeling hogs production and growth at a finer temporal resolution (QPRC) in Washington, DC. USA. Available at: http://fs2.american.edu/baron/www/QPRCpresentations/Sartore_Cont_C.pdf

Benecha, H., Sartore, L., Cruze, N. (2019) Model-based crop yield forecasting; covariate selection and related issues (JSM) in Denver, CO. USA.

Rosales, A., Sartore, L., Johnson, D. (2019) Beyond NDVI: Impact of distribution characteristics of alternative remotely-sensed data on county maize yield estimates (ICAS) in Delhi. India.

Wei, Y., Sartore, L., Sedransk, N. (2019) Using generative adversarial networks to generate synthetic population (JSM) in Denver, CO. USA.

Wei, Y., Sartore, L., Sedransk, N. (2019) Web scraping-natural language processing for disease outbreak detection and information extraction (QPRC) in Washington, DC. USA.

Sartore, L. (2018) Future of integer calibration weighting methods (SDSS) in Reston VA. USA. Available at: https://ww2.amstat.org/meetings/sdss/2018/onlineprogram/ViewPresentation.cfm?file=304327.pdf

Sartore, L., Cruze, N., Benecha, H., Erciulescu, A., Toppin, K., Spiegelman, C. (2018) Ad-hoc calibration for rounding rules with nonlinear benchmarks (JSM) in Vancouver, BC. Canada.

Sartore, L., Toppin, K. and Spiegelman, C. (2018) inca: an R package for integer calibration (GASP) in Washington, DC. USA. Available at: http://washstat.org/presentations/20181024/Sartore.pdf

Sartore, L., Toppin, K. and Spiegelman, C. (2018) The advantage of integer calibration in the age of big data (NISS Big Data Workshop at NCES) in Washington, DC. USA.

Toppin, K., Sartore, L., Spiegelman, C. (2018) A global convergent algorithm for integer calibration weighting (JSM) in Vancouver, BC. Canada.

Wei, Y. Sartore, L., Abernethy, J., Miller, D., Toppin, K., Spiegelman, C. (2018) Deep learning for data imputation and calibration weighting (JSM) in Vancouver, BC. Canada.

Wei, Y., Sartore, L. and Sedransk, N. (2018) Using Gaussian copula to generate a synthetic population (GASP) in Washington, DC. USA. Available at: http://washstat.org/presentations/20181024/Wei.pdf

Sartore, L., Benecha, H., Toppin, K., Spiegelman, C. (2017) Restricted multinomial regression for a triple-system estimation with list dependence (JSM) in Baltimore MD, USA.

Abernethy, J., Sartore, L., Benecha, H., Spiegelman, C. (2017) Estimation of capture probabilities by accounting for sample designs (JSM) in Baltimore MD. USA.

Benecha, H., Abreu, D.A., Abernethy, J., Sartore, L., Young, L.J. (2017) Evaluation of a new approach for estimating the member of US farms (JSM) in Baltimore MD, USA.

Toppin, K., Sartore, L., Spiegelman, C. (2017) Design weights and calibration (JSM) in Baltimore MD, USA.

Sartore, L., Toppin, K. and Spiegelman, C. (2016) Estimated covariance matrices associated with calibration (JSM) in Chicago IL. USA.

Sartore, L., Toppin, K. and Spiegelman, C. (2016) Integer programming for calibration (FedCASIC) in Suitland MD. USA. Available at: https://www.census.gov/fedcasic/fc2016/ppt/1_5_Integer.pdf

Sartore, L., Toppin, K. and Spiegelman, C. (2016) Introducing a new calibration procedure for the Census of Agriculture (JSM) in Chicago IL. USA.

Yazdani, A., Yazdani, A., Sartore, L. and Boerwinkle, E. (2016) Identification of genetic effects on abnormalities of cardiac structure and function using Bayesian hierarchical model (JSM) in Chicago IL. USA.

Borrotti, M., Sartore, L. and Slanzi, D. (2014) Intelligent control system model-based optimisation for energy saving (ERCIM) in Pisa. Italy.

Fabbri, P., Piccinin, I. and Sartore, L. (2014) Hydrostratigraphical modeling using a 3D geostatistical approach (IAH) in Marrakech. Morocco.

Sartore, L. (2013) Constrained simultaneous nonlinear quantile regression (DAGStat) in Freiburg. Germany.

Service to the Profession

  • Organizer of an invited session at the 2025 Joint Statistical Meeting (JSM): Government Research on Predictive Analytics and Uncertainty.
  • Organizer of a topic-contributed session at the 2025 Joint Statistical Meeting (JSM): Innovation in Official Statistics: A Journey from Sampling to Publication.
  • NASS representative member of the organizing committee of the 2024 Artificial Intelligence User Forum (AIUF).
  • Panelist at the 2024 Artificial Intelligence User Forum (AIUF): Practical Applications of Artificial Intelligence across the United States Department of Agriculture (USDA) Research Portfolio.
  • Judge for the Harvard Data Science Initiative at the Agri-Datathon (https://datascience.harvard.edu/2024/10/25/).
  • Chair of a topic-contributed session at the 2024 Joint Statistical Meeting (JSM): Artificial Intelligence in Federal Statistics: Current Uses, Potential Applications, and Ongoing Issues.
  • Chair of a special event at the 2024 Symposium on Data Science and Statistics (SDSS): National Institute of Statistical Science (NISS) and Federal Committee on Statistical Methodology (FCSM): Artificial Intelligence in the Federal Government.
  • Panelist at the 2024 Georges’ STAT DAY: Opportunities and Challenges for Statisticians in the Era of Artificial Intelligence.
  • NISS/NASS liaison in the organizing committee of the 2024 NISS workshop on artificial intelligence.
  • Chair of a topic-contributed session at the 2023 Joint Statistical Meeting (JSM): Methodological Developments to Inform Medical Decision Making.
  • Chair of a session at the 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Surface Biophysical Parameters.
  • Mentor of early-career statisticians at the 2022 Symposium on Data Science and Statistics (SDSS): Working as a consultant in a nonprofit organization.
  • Organizer of a topic-contributed session at the 2018 Joint Statistical Meeting (JSM): Leading the Estimates Towards Known Benchmarks.

Reviewer (Referee)

Reviewer (referee) for the following academic journals:

  • Agronomy (an MDPI Journal)
  • Applied Computing and Geosciences
  • Applied Computing and Informatics
  • Artificial Intelligence in Agriculture
  • Computation (an MDPI Journal)
  • Computational Statistics
  • Computers and Electronics in Agriculture
  • Engineering Computations
  • Entropy (an MDPI Journal)
  • Mathematics (an MDPI Journal)
  • SSSA (Soil Science Society of America) Journal
  • Statistical Journal of the IAOS (International Association for Official Statistics)
  • Statistics in Transition

Membership in Academic and Professional Societies

  • American Statistical Association (ASA) since 2015.
  • Institute of Electrical and Electronics Engineers (IEEE) since 2024.
  • Society of Industrial and Applied Mathematics (SIAM) since 2024.

Languages

Italian: native

Venetian: native

English: fluent (written/spoken)

German: basic (written/spoken)

Publications