Directory

Maria Weese

Associate Professor

Information Systems & Analytics


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

Recent Publications

  • Martinez, M.G., Weese, M.L., Jones-Farmer, L.A., A One Class Peeling method for multivariate outlier detection with application to Phase I Monitoring. (2020) Quality and Reliability Engineering International. 36(4):1272:1295
  • Smucker, B.J., Edwards, D.J., Weese, M.L., Response Surface Models: To Reduce or Not to Reduce?. In Press: Journal of Quality Technology.
  • Weese, M.L., Montgomery, D.J., Ramsey,P.J., (2017) Analyzing Definitive Screening Designs: Screening vs. Prediction. Applied Stochastic Models in Business and Industry. 34(2):244-255.
  • Ockuly, R. A., Weese, M.L., Smucker, B.J., Edwards, D.J., Chang, L. (2017) ``Response Surface Experiments: A Meta-Analysis`` Chemometrics and Intelligent Laboratory Systems. 164, 64-75.
  • Weese, M. L., Smucker, B. J., Edwards, D. J., (2017) "\A Criteria for Constructing Powerful Supersaturated Designs when Effect Directions are Known". Journal of Quality Technology. 49(3):265-277.
  • Weese, M.L., Martinez, W.G., and Jones-Farmer, L.A. (2016) On the Selection of the Bandwidth Parameter for the k-Chart. Quality and Reliability Engineering International. 33(7):1527-1547.
  • Campbell, J.T., Weese, M.L., (2016) "Executive Pay as a Mixture: Compositional Models and the Influence of CEO Pay on Firm Performance". Organizational Research Methods 20(1), 95-120.

Honors & Awards

  • 2017: Nominee for Outstanding Professor
  • 2014: Nominee for Outstanding Professor
  • 2014 Best Presentation Honorable Mention, Joint Statistical Meetings
  • 2013-2014 CELTUA Faculty/Staff Commendation for Teaching
  • 2012-2013 CELTUA Faculty/Staff Commendation for Teaching
  • 2009: Winner of the SPES Joint Statistical Meetings Student Poster Competition for An EDA Approach to the Study of Multivariate Process Data
  • 2009-2010: Haslam College of Business Administration ESPN Scholarship, University of Tennessee

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 Miami University 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 225 M WEB
  • ISA 591 A/B 11:40-1 MW FSB 2050 HYB
  • ISA 491 B 1:15-2:35 MW FSB 2050 HYB
Maria Weese

Contact Information

Office Hours

  • Virtually by appointment

Links

* Accessible version of PDF available upon request.