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David Newton (Fed)

Mathematical Statistician

David Newton was born in Georgia and grew up in South Carolina. He received a B.S. in Mathematics with a minor in Computer Science from Centre College (2015), and an M.S. (2017) and Ph.D. (2023) in Statistics from Purdue University. At Purdue, David served as an instructor, research assistant, and data science consultant. David began work as a Mathematical Statistician at NIST in the fall of 2020. During his time at NIST, he has enjoyed working on a variety of interdisciplinary projects with NIST scientists, including applications in cell counting, atom probe tomography, and phase fraction measurements, among other areas. He is particularly interested in Bayesian modeling, machine learning, and the development and deployment of statistical software applications.


  • 2023 NIST Bronze Medal Award
  • 2023 ITL Outstanding Technology Transfer Award
  • 2022 DOC Gold Medal Award

Selected Publications

  • Laura Pierce, David Newton, Steven Lund, Sumona Sarkar. A Guided Demonstration of the Counting Method Evaluation Tool (COMET) for Implementing the ISO 20391-2 Cell Counting Standard for Cell-based Therapies. Cell & Gene Therapy Insights 2023; 9(5), 581–609.
  • Gerald Fattah, David Newton, Helen Qiao, Dennis Leber. Anomaly Detection for Industrial Robot Prognostics and Health Management. ASME 2023 18th International Manufacturing Science and Engineering Conference. MSEC2023-104888.
  • Shruthi Suresh, David T. Newton, Thomas H. Everett IV, Guang Lin, and Bradley S. Duerstock. Feature Selection Techniques for Machine Learning Model to Detect Autonomic Dysreflexia. Frontiers in Neuroinformatics. Volume 16 - 2022.
  • Yongyang Huang, Jordan Bell, Dmitry Kuksin, Sumona Sarkar, Laura T Pierce, David Newton, Jean Qiu, Leo Li-Ying Chan. Practical application of cell counting method performance evaluation and comparison derived from the ISO Cell Counting Standards Part 1 and 2. Cell & Gene Therapy Insights 2021; 7(9), 937–960.
  • Shirley Rietdyk, Satyajit Ambike, Steve Amireault, Jeffrey M. Haddad, Guang Lin, David Newton, Elizabeth A. Richards. Co-occurrences of fall-related factors in adults aged 60 to 85 years in the United States National Health and Nutrition Examination Survey. PLoS One 2022 Nov 8;17(11):e0277406.
  • David Newton, Raghu Bollapragada, Raghu Pasupathy, Nung Kwan Yip. Retrospective Approximation for Smooth Stochastic Optimization. arXiv:2103.04392. Optimization and Control. Submitted March 2021.
  • Antonio Possolo, Amanda Koepke, David Newton, Michael Winchester. Decision Tree for Key Comparisons. Journal of Research of National Institute of Standards and Technology. Volume 126, Article No. 126007 (2021).
  • Enrico Lucon, Jolene Splett, Amanda Koepke, David Newton. NIST Software Package for Obtaining Charpy Transition Curves. Technical Note (NIST TN) - 2158.
  • Frederick Damen, David Newton, Guang Lin, Craig Goergen. Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data. Applied Sciences. 2021, 11(4), 1690;
  • David Newton, Farzad Yousefian, Raghu Pasupathy. Stochastic Gradient Descent: Recent Trends. INFORMS TutORials in Operations Research. 19 Oct 2018. 


Decision Tree for Key Comparisons

Antonio Possolo, Amanda Koepke, David Newton, Michael R. Winchester
This contribution describes a Decision Tree intended to guide the selection of statistical models and data reduction procedures in key comparisons (KCs). The
Created September 15, 2020, Updated January 26, 2024