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FSB Directory

Maria Weese

Professor & FSB Faculty Fellow & MSBA Faculty Director

Information Systems & Analytics


Maria Weese

Contact Information

Office Hours

  • TR 12:30 - 1:00
  • W 8:00 - 9:00

Links

  • [PDF]*

* Accessible version of PDF available upon request.

Profile

Academic Background

  • Ph.D. University of Tennessee, Statistics, 2010
  • M.S. University of Tennessee, Statistics, 2006
  • B.A. Virginia Tech, Chemical Engineering, 2001

Academic & Professional Experience

  • Associate Professor, Department of Information Systems & Analytics, 91做厙 (Aug. 2020-present)
  • Affiliate Faculty Member, Department of Statistics, 91做厙 (May 2019-present)
  • Richard T. Farmer Assistant Professor, Department of Information Systems & Analytics, 91做厙 (Aug. 2018-July 2020)
  • Assistant Professor, Department of Information Systems & Analytics, 91做厙 (Aug. 2014-July 2018)
  • Lecturer, Department of Information Systems & Analytics, 91做厙 (Aug. 2012- July 2014)
  • Visiting Assistant Professor, Department of Decision Sciences and Management Information Systems, 91做厙 (Aug. 2010-July 2012)
  • Statistical Consultant, Statistical Consulting Center, University of Tennessee (2004-2006)
  • Process Improvement Engineer II, Celanese Acetate (2001-2004)

Recent Publications

  • Weese, M.L., Smucker, B.J., & Edwards, D.J. (2026). The use of cross validation in the analysis of designed experiments. Accepted: Quality Engineering.
  • Smucker, B. J., Wright, S. E., Williams, I., Page, R. C., Kiss, A. J., Silwal, S. B., Weese, M.L., & Edwards, D. J. (2025). Large Row-Constrained Supersaturated Designs for High-throughput Screening. Biometrics, 81(4), 160. [https://doi.org/10.1093/biomtc/ujaf160](https://doi.org/10.1093/biomtc/ujaf160)
  • Smucker, B.J., Edwards, D.J., & Weese, M.L. (2025). Supersaturated designs for main effects and two-factor interactions. Quality and Reliability Engineering International, 42(1), 52-56. [https://doi.org/10.1002/qre.70071](https://doi.org/10.1002/qre.70071)
  • Stallrich, J. W., Young, K., Weese, M.L., Smucker, B. J., & Edwards, D. J. (2025). An optimal design framework for lasso sign recovery. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 87(5), 1457-1480. [https://doi.org/10.1093/jrsssb/qkaf026](https://doi.org/10.1093/jrsssb/qkaf026)
  • Arafat, S.繒, Sun, N.繒, Weese, M.L., & Martinez, W. G. (2025). Boundary peeling: An outlier detection method. Quality Engineering, 37(4), 583-596. [https://doi.org/10.1080/08982112.2025.2469518](https://doi.org/10.1080/08982112.2025.2469518)
  • Young, K.繒, Weese, M.L., Stallrich, J.W., Smucker, B.J., & Edwards, D. J. (2024). A graphical comparison of screening designs using support recovery probabilities. Journal of Quality Technology, 56(4), 355-368.

Honors & Awards

  • 2025: Shwell Award Winner, Fall Technical Conference
  • 2025-2027: Farmer School of Business Research Fellow
  • 2024: 91做厙 Creativity and Innovation Award
  • 2022: Lloyd S. Nelson Award, Journal of Quality Technology
  • 2022: 91做厙 Prodesse Quam Conspcici Award
  • 2017: Associated Student Gonvernment Nominee for Outstanding Professor
  • 2014: Associated Student Government Nominee for Outstanding Professor
  • 2014: Best Presentation Honorable Mention, Joint Statistical Meetings

Professional Interests

  • Research: Analysis based Design, Data Stream Monitoring, Screening Design, Optimal Supersaturated Design

Biography

Dr. Maria Weese earned a bachelors degree in Chemical Engineering from Virginia Tech and worked for three years as a Process Improvement Engineer for Celanese Acetate before returning to graduate school to pursue graduate studies in Statistics at the University of Tennessee. Maria is an active researcher in the areas of design of statistical experiments and statistical monitoring. As such she has high quality publications in both of those areas. Dr. Weese has been in the Farmer School of Business 91做厙 since receiving her PhD in 2010 and has taught extensively in the Business Analytics program developing the current Business Analytics Practicum course as well as Statistical Monitoring and Design of Experiments and Introduction to Data Mining.

Courses

  • ISA 391B TR 8:30 - 9:50 FSB 0031
  • ISA 391C TR 10:05 - 11:25 FSB 0031