Mary Kathryn (Kate) Cowles

Mary Kathryn (Kate) Cowles
Professor of Statistics and Actuarial Science, College of Liberal Arts and Sciences
Professor of Biostatistics, College of Public Health

Contact Information

Primary Office: 241 Schaeffer Hall (SH)
The University of Iowa
Iowa City, IA 52242
319-335-0727

Office Hours

Monday: 12:30 pm - 1:15 pm
Wednesday: 10:30 am - 11:15 am
Thursday: 1:30 pm - 2:15 pm

Biography

Kate Cowles received her Ph.D. in Biostatistics from the University of Minnesota. She is a Professor of Statistics and Biostatistics at The University of Iowa. Her research interests include Bayesian modeling, statistical computing, and environmental and spatial statistics.

Education

  • PhD in Biostatistics, University of Minnesota,, Minneapolis, Minnesota, United States, 1994
  • MS in Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States, 1990
  • MM in Piano performance and pedagogy, Northwestern University, Evanston, Illinois, United States, 1972
  • BA in Music, Carleton College, Northfield, Minnesota, United States, 1971

Areas of Research Interest

  • Bayesian Statistics
  • Computational Statistics
  • Environmental and Spatial Statistics

Selected Professional Memberships

  • American Statistical Association

Selected Awards and Honors

  • President and Provost Award for Teaching Excellence,, The University of Iowa, 2015
  • CLAS Collegiate Teaching Award, University of Iowa, 2011
  • James N. Murray Faculty Award, University of Iowa, 2001

Selected Courses Taught

  • Seminar Applied Statistics, STAT:7290, Spring 2018
  • Readings in Statistics, STAT:6990, Spring 2018
  • Reading Research, STAT:7990, Spring 2018
  • Large Data Analysis, STAT:4740, Spring 2018
  • Readings in Statistics, STAT:6990, Fall 2018
  • Computing in Statistics, STAT:5400, Fall 2018
  • Bayesian Statistics, STAT:4520, Fall 2018

Selected Publications

  • Cowles, M. (2017). Independent Sampling for Bayesian Normal Conditional Autoregressive Models with OpenCL Acceleration. Computational Statistics/Springer. DOI: https://doi.org/10.1007/s00180-017-0752-0.
  • Liang, D., Cowles, M. & Linderman, M. (2016). Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR. Remote Sensing of Environment, 186, 393-404. DOI: http://dx.doi.org/10.1016/j.rse.2016.09.002.
  • Abban, B. K., Papanicolaou, A. N., Cowles, M., Wilson, C. G., Abaci, O., Wacha, K., Schilling, K. & Schnoebelen, D. (2016). An Enhanced Bayesian Fingerprinting Framework for Studying Sediment Source Dynamics in Intensively Managed Landscapes. Water Resources Research, 52, 4646-4673. DOI: 10.1002/2015WR018030.
  • Cowles, M. K. (2013). Applied Bayesian Statistics with R and OpenBUGS Examples. Springer.
  • Bayman, E. O., Chaloner, K. & Cowles, M. K. (2010). Detecting qualitative interaction: A Bayesian approach. Statistics in Medicine, 29(4), 455-463. DOI: 10.1002/sim.3787.
  • Cowles, M., Yan, J. & Smith, B. J. (2009). Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models. Journal of Computational and Graphical Statistics, 18(2), 262-282. DOI: 10.1198/jcgs.2009.08012.
  • Smith, B. J., Yan, J., Cowles, M. (Eds.) (2008). Unified Geostatistical Modeling for Data Fusion and Spatial Heteroskedasticity with R Package ramps. Journal of Statistical Software, 25(10), 1-21. DOI: 10.18637/jss.v025.i10.
  • Smith, B. J., Cowles, M. (2007). Correlating Point-Referenced Radon and Areal Uranium Data Arising from a Common Spatial Process. Journal of the Royal Statistical Society Series C -- Applied Statistics, 56(3), 313-326. DOI: 10.1111/j.1467-9876.2007.00579.x.
  • Gaul, N. J., Cowles, M., Choi, K. K. & Lamb, D. (2016). Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis. (Vols. 2B). INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2015, VOL 2B.
  • Cowles, M. K., Seedorff, M. & Sawyer, A. (2013). CARrampsOcl: Reparameterized and marginalized posterior sampling for conditional autoregressive models, OpenCL implementation.

Selected Presentations

  • Cowles, M. (2016, July) Harnessing Heterogeneous Hardware for Affordable, Portable Bayesian Computing. Conference Presentation presented at Joint Statistical Meetings 2016, Chicago, Illinois.
  • Cowles, M. (2013, April) Discussion of Revealing Latent Clusters from Dirichlet Process Mixture Models Using Product Partitions. Invited Lecture presented at SLAMM! 2013. Saint Louis Area Methods Meeting, Iowa City, Iowa.
  • (2009, August) Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models. Invited Lecture presented at Joint Statistical Meetings, American Statistical Association, Washington, District of Columbia.
  • (2007, August) Bayesian Evaluation of Surrogate Endpints in Cinical Trials. Invited Lecture presented at Joint Statistical Meetings, American Statistical Association, Salt Lake City, Utah.
  • (2007, June) Computing for Spatial Estimation and Prediction with Application to Residential Radon. Invited Lecture presented at DIMACS Workshop on Markov Chain Monte Carlo, DIMACS, Piscatway, New Jersey.
  • (2006, April) Fusing Point-Referenced Radon Data with Areal Uranium Data Arising from a Common Process. Invited Lecture presented at Interface 2006, Interface of Statistics and Computing, Pasadena, California.

Selected Grants and Contracts

  • Oliveira, Suely (Principal Investigator), Darcy, Isabel (Co-Principal), Stewart, David (Co-Principal), Cowles, Mary Kathryn (Co-Principal) Grant Equipment. EXTREEMS-QED: Large Data Analysis and Visualization. Sponsored by National Science Foundation. Funded. August 15, 2014 - August 14, 2019.
  • Cowles, Mary (Principal Investigator), Bennett, David (Co-Principal), Kusiak, Andrew (Co-Principal), Segre, Alberto (Co-Principal), Stewart, Kathleen (Co-Principal) Grant Research/Creative Work (Applied or Basic). Integrated Graduate Research and Education Traineeship (IGERT): Geoinformatics for Environmental and Energy Modeling and Prediction (GEEMaP). Sponsored by National Science Foundation. Funded. July 1, 2010 - June 30, 2016.
Last Modified Date: January 30, 2019