Department of Statistics and Operations Research,
University of North Carolina,
Chapel Hill, NC 27599-3260.

Office: 303 Hanes Hall
Email: rls at email dot unc dot edu

• • • Richard L. Smith
      • Mark L. Reed III Distinguished Professor

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        Richard L. Smith is Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics in the University of North Carolina, Chapel Hill. From 2010-2017 he was Director of the Statistical and Applied Mathematical Sciences Institute, a Mathematical Sciences Institute supported by the National Science Foundation. From January-June 2018, he was Associate Director of SAMSI. He obtained his PhD from Cornell University and previously held academic positions at Imperial College (London), the University of Surrey (Guildford, England) and Cambridge University. His main research interest is environmental statistics and associated areas of methodological research such as spatial statistics, time series analysis and extreme value theory. He is particularly interested in statistical aspects of climate change research, and in air pollution including its health effects. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and has won the Guy Medal in Silver of the Royal Statistical Society, and the Distinguished Achievement Medal of the Section on Statistics and the Environment, American Statistical Association. In 2004 he was the J. Stuart Hunter Lecturer of The International Environmetrics Society (TIES). He is also a Chartered Statistician of the Royal Statistical Society. In 2020, he was elected a Fellow of the American Association for the Advancement of Science (AAAS).

        

        Recent Publications:

        Smith, R.L. (2025), Detection and Attribution of Extreme Weather Events: A Statistical Review. To appear in Handbook on Statistics of Extremes, Chapman & Hall / CRC series on Handbook of Modern Statistical Methods, Miguel de Carvalho, Raphael G. Huser, Philippe Naveau and Brian J Reich (editors).

        Power, M.C., Lynch, K.M., Bennett, E.E., Ying, Q., Park, E.S., Xu, X., Smith, R.L., Stewart, J.D., Yanosky, J.D., Duanping, L., van Donkelaar, A., Kaufman, J.D., Sheppard, L., Szpiro, A.A., Whitsel, E.A.. (2024) A Comparison of PM2.5 Exposure Estimates from Different Estimation Methods and their Associations with Cognitive Testing and Brain MRI Outcomes. Environmental Research

        Benjamin Leinwand, Puyao Ge, Vidyadhar Kulkarni, Richard Smith (2021), Winning an election, not a popularity contest. Significance, Vol. 18 Number 4, pages 24-28.

        Huang, H., Hammerling, D., Li, B. and Smith, R.L. (2020), Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach Arxiv. Preprint, submitted for publication.

        Russell, B., Risser, M., ,Smith, R.L. and Kunkel, K.E. (2020), Investigating the association between late spring Gulf of Mexico sea surface temperatures and US Gulf Coast precipitation extremes with focus on Hurricane Harvey Environmetrics, Vol. 31, issue 2, March 2020, paper e2595 (posted online July 23, 2019) . Supplementary Materials

        Gelfand, A., Fuentes, M., Hoeting, J. and Smith, R.L. (editors), Handbook of Environmental and Ecological Statistics. Chapman and Hall/CRC Handbooks of Modern Statistical Methods, Published February 11, 2019.

        

        Full CV

        

        Teaching Webpages (Current):

        Spring 2025: STOR 834, Extreme Value Theory

        

        Research Webpages:

        Preprints

        Conference Talks and Seminar Presentations

        Ph.D. Theses

        Climate Extremes project

        Boston Marathon project

        California Air Pollution project

        Short Course in Kolkata, March 2015

        Lectures on Extreme Value Theory given at North Carolina State University, 2015

        Project on Marathon Times and Ages

        

        Teaching Webpages (Older):

        Fall 2024: STOR 557, Advanced Methods of Data Analysis

        Spring 2024: STOR 151, Introduction To Data Analysis

        Fall 2023: STOR 557, Advanced Methods of Data Analysis

        Fall 2023: STOR 664, Applied Statistics I

        Spring 2023: STOR 834, Extreme Value Theory

        Fall 2022: STOR 557, Advanced Methods of Data Analysis

        Fall 2022: STOR 664, Applied Statistics I

        Fall 2021: STOR 557, Advanced Methods of Data Analysis

        Fall 2021: STOR 664, Applied Statistics I

        Spring 2021: STOR 455, Methods of Data Analysis

        Fall 2020: STOR 590, Advanced Linear Models

        Fall 2020: STOR 664, Applied Statistics I

        Fall 2020: Data Science for COVID-19

        Spring 2020: STOR 590, Advanced Linear Models

        Spring 2020: STOR 834, Extreme Value Theory

        Spring 2019: STOR 556, Advanced Methods of Data Analysis

        Spring 2019: STOR 754, Time Series and Multivariate Analysis

        Fall 2017 SAMSI class on Statistical Methods for Climate Research

        STOR 664: Applied Statistics I, Fall 2009.

        STOR 940: SAMSI course on Spatial Epidemiology, Fall 2009.

        STOR 151 (Basic Concepts of Statistics), Spring 2010.

        STOR 654 (Time Series and Multivariate Analysis), Fall 2008.

        STOR 890 (Environmental Statistics), Spring 2009.

        Environmental Statistics Lecture Notes (2001).

        STOR 356, Spring 2008.

        Statistics 174: Applied Statistics I, Fall 2004

        

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        Webpage updated June 9, 2022