Nathaniel Stevens

Nathaniel Stevens
Associate Professor of Statistics, Associate Chair-Undergrad, Director of Undergraduate Data Science
Location: M3 3143
Phone: 519-888-4567, ext. 45540
Status: Active

Biography

Nathaniel is an Associate Professor of Statistics at the University of Waterloo (UW) in the Department of Statistics and Actuarial Science, and he is the Director of the BMATH and BCS in Data Science programs. He is also the Past President of the Statistical Society of Canada's Data Science and Analytics Section. From 2015-2018 Nathaniel held a faculty position at the University of San Francisco (USF) where he was jointly appointed in the Department of Mathematics and Statistics as well as the MS in Data Science program. From 2017-2018 he served as Program Director for their BS in Data Science program. Prior to this, Nathaniel earned BMATH (2010), MMATH (2011) and PhD (2015) degrees in Statistics from the University of Waterloo.

During his career, Nathaniel has also worked as a consultant and instructor within USF's Data Institute and UW's Business and Industrial Statistics Research Group (BISRG). Nathaniel is currently the Director of BISRG. In these capacities he has provided professional training and worked on several projects resulting in collaborations with 20+ organizations. He has also overseen 30+ undergraduate and graduate level data science internships in exploratory data analysis, time series analysis, machine learning, A/B testing and data visualization.

In general, Nathaniel is interested in using data to make decisions, solve problems, and improve processes. Specifically, his research interests lie at the intersection of data science and industrial statistics. He is interested in methodological developments in experimental design and A/B testing, process monitoring and network surveillance, reliability and survival analysis, and the assessment and comparison of measurement systems. Recently Nathaniel has developed a family of comparative probability metrics that may be used in place of traditional hypothesis tests in any setting that requires a comparison of statistical quantities. Applications include the comparison of experimental conditions, the comparison of measurement systems, the comparison of survival experiences and more generally, the comparison of expected and predicted response surfaces.

Nathaniel was the recipient of the 2023 ENBIS Young Statistician Award and the 2023 ASQ Feigenbaum Medal. He also received the 2021 Søren Bisgaard for "Bayesian Probability of Agreement for Comparing Survival or Reliability Functions with Parametric Lifetime Regression Models" which was judged to be the Quality Engineering paper with the greatest potential for advancing the practice of quality improvement. He was also the recipient of the 2020 ASQ Llyod Nelson Award for "Design and Analysis of Confirmation Experiments" which was deemed to be the paper appearing in the Journal of Quality Technology with the greatest immediate impact to practitioners. His talk "Comparing Two Kaplan-Meier Curves with the Probability of Agreement" was awarded the 2019 Shewall Award for best talk at the ASQ Fall Technical Conference. He was also the recipient of the 2017-2018 Best Reliability Paper in Quality Engineering for "Quantifying Similarity in Reliability Surfaces Using the Probability of Agreement".

Nathaniel is extremely passionate about teaching. His approach is founded on respect and a desire to see his students succeed both in and outside the classroom. He appreciates the importance and the value of each student’s education and he endeavours to provide the high-quality educational experience that every student deserves. His devotion in this area has earned him the 2022 Math Faculty Distinction in Teaching Award, the 2020 Dept. of Statistics and Actuarial Science Distinction in Teaching Award, and a nomination for the 2021 WUSA (Waterloo Undergraduate Student Association) Excellence in Undergraduate Teaching Award.

Education

  • 2011–2015, PhD, Statistics, University of Waterloo, Canada

  • 2010–2011, Masters of Mathematics, Statistics, University of Waterloo, Canada

  • 2005–2010, Bachelor of Mathematics, Statistics, University of Waterloo, Canada

Teaching*

  • STAT 341 - Computational Statistics and Data Analysis
    • Taught in 2021, 2022, 2023, 2024
  • STAT 430 - Experimental Design
    • Taught in 2021, 2022
  • STAT 830 - Experimental Design
    • Taught in 2021, 2022
  • STAT 938 - Statistical Consulting
    • Taught in 2021, 2022, 2023, 2024

* Only courses taught in the past 5 years are displayed.

Selected/Recent Publications

  • Nadi, A. Ahmadi, Steiner, S. H., & Stevens, N. T.. (2024). Assessing Measurement System Agreement in the Presence of Reproducibility and Repeatability. Technometrics. Retrieved from https://doi.org/10.1080/00401706.2023.2296465

  • Pan, R., Lu, L., & Stevens, N.. (2024). Editorial for reliability engineering special issue. Quality Engineering. Retrieved from https://doi.org/10.1080/08982112.2024.2293610

  • Rigdon, S. E., Stevens, N. T., Wilson, J. D., & Woodall, W. H.. (2024). First to signal criterion for comparing control chart performance. Quality Engineering, 36(2), 427-438. Retrieved from https://doi.org/10.1080/08982112.2023.2223690

  • Sen, A., Stevens, N. T., N Tran, K., Agarwal, R. R., Zhang, Q., & Dubin, J. A.. (2023). Forecasting Daily COVID-19 Cases with Gradient Boosted Trees and Other Methods: Evidence from U.S. Cities. Frontiers in Public Health, in press.

  • Larsen, N., Stallrich, J. W., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N. T.. (2023). Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. The American Statistician, in press.

  • Smucker, B. J., Stevens, N. T., Asscher, J., & Goos, P.. (2023). Profiles in the Teaching of Experimental Design and Analysis. Journal of Statistics and Data Science Education, 31(3), 211—224.

  • Bui, T., Steiner, S. H., & Stevens, N. T.. (2023). General Additive Network Effect Models. The New Journal of Statistics in Data Science, 1(3), 342–360.

  • Stevens, N. T.. (2023). Is designed data collection still relevant in the big data era? A discussion. Quality and Reliability Engineering, 39(4), 1107–1109.

  • Cook, C. M. Anderso, Lu, L., Brenneman, W., de Mast, J., Faltin, F., Freeman, L., Guthrie, W., et al. (2022). Statistical Engineering – Part 2: Future. Quality Engineering, 34(4), 446–467.

  • Anderson-Cook, C. M., Lu, L., Brenneman, W., de Mast, J., Faltin, F., Freeman, L., Guthrie, W., et al. (2022). Statistical Engineering – Part 1: Past and Present. Quality Engineering, 34(4), 426–445.

  • Lu, L., Anderson-Cook, C. M., Stevens, N. T., & Hagar, L.. (2022). Using a Baseline with the Probability of Agreement to Compare Distribution Characteristics. Quality Engineering, 34(3), 322–343.

  • Stevens, N. T., & Hagar, L.. (2022). Comparative probability metrics: Using posterior probabilities to account for practical equivalence in A/B tests. The American Statistician, 76(3), 224-237.

  • Stevens, N. T., Sen, A., Kiwon, F., Morita, P. P., Steiner, S. H., & Zhang, Q.. (2022). Estimating the effects of non-pharmaceutical intervent and population mobility on daily COVID-19 cases: Evidence from Ontario. Canadian Public Policy, 48(1), 144–161.

  • Yu, L., Zwetsloot, I. M., Stevens, N. T., Wilson, J. D., & Tsui, K. L.. (2022). Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study. Quality and Reliability Engineering International, 38(3), 1226–1250.

  • Stevens, N. T., & Wilson, J. D.. (2021). The past, present, and future of network monitoring: A panel discussion. Quality Engineering, 33(4), 715–718.

  • Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Foundations of network monitoring: Definitions and applications. Quality Engineering, 33(4), 719–730.

  • Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Paris, C., Paynabar, K., Perry, M. B., et al. (2021). The interdisciplinary nature of network monitoring: Advantages and disadvantages. Quality Engineering, 33(4), 731–735.

  • Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Research in network monitoring: Connections with SPM and new directions. Quality Engineering, 33(4), 736–748.

  • Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Broader impacts of network monitoring: Its role in government, industry, technology, and beyond. Quality Engineering, 33(4), 749–757.

  • Motalebi, N., Stevens, N. T., & Steiner, S. H.. (2021). Hurdle blockmodels for sparse network modeling. The American Statistician, 75(4), 383–393.