Breadcrumb

Yusuf K. Bilgiç

Assistant Professor
South Hall 324A
245-5484
bilgic@geneseo.edu

Yusuf K. Bilgiç has been a member of the Geneseo faculty since 2012. 

Did you know UP-STAT 2018 Conference is in Rochester (UR), April 20-21?

Yusuf-Bilgic

Office hours and course websites

Curriculum Vitae

Education

  • Ph.D. in Statistics, Western Michigan University, 2012

  • M.A. in Teaching Math, Western Michigan University, 2008

Publications

  • Bilgic, Y., Morgan, B., Petakos, K. 2017. Using ​ ​Sociocultural ​ ​Theory ​ ​and ​ ​Group ​ ​Interviews ​​to ​ ​Support ​ ​Students' ​ ​Understanding ​ ​of Concepts ​ ​from ​ ​Conditional ​ ​Probability. Submitted.

  • Urso, A., Bilgic Y. K., Reuter, G., Riccomini, P. J. 2017. Instructional Supports for Struggling Students in Probability and Statistics Instruction. Submitted.

  • Bilgic Y. K., McKean J. W., Kloke J. D., and Abebe A. Iteratively reweighted generalized rank-based methods for hierarchical mixed models. In progress.

  • Abebe Asheber, McKean Joseph W., Kloke John D., and Bilgic Yusuf K. 2016. Iterated Reweighted Rank-Based Estimates for GEE Models. Robust Rank-Based and Nonparametric Methods, Volume 168 of the Series Springer Proceedings in Mathematics & Statistics, pp 61-79, Book Chapter. Liu R. and McKean J. (Eds.), Springer International Publishing.

  • LaVigne* N., Bilgic Y. 2015. Robust Estimators In Fuzzy Logic Cellular-Automata Salt And Pepper Noise Filtering. International Journal of Computers and Technology. Vol 15, No 1.

  • Bilgic, Y. K., J., Susmann, H., McKean. 2014. rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models. URL http://CRAN.R-project.org/package=rlme. R package version 0.4.1

  • Jon D. Davis, Dustin O. Smith, Abhik R. Roy, Yusuf K. Bilgic. 2014. Reasoning-and-proving in algebra: The case of two reform-oriented U.S. textbooks, International Journal of Educational Research, Volume 64, 2014, Pages 92-106, ISSN 0883-0355, http://dx.doi.org/10.1016/j.ijer.2013.06.012.

  • Bilgic, Y. K., Susmann, H. 2013. rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models. The R Journal, 5(2):71?80.

  • Bradley, C., DeBose C. H., Terpstra, J. T. and Bilgic, Y. K. 2012. Postadoption Services Utilization Among African American, Transracial, and White American Parents: Counseling and Legal implications. The Family Journal. October 2012 Vol. 20 No. 4 392-398.

  • Bilgic, Y. K. Rank-Based Estimation and Prediction for Mixed Effects Models in Nested Designs. Ph.D. Thesis. 2012. WMU.

More About Me

Research Interests

Probability, Statistics and Data Science (PSDS) education for grades 6 to 16; Nonparametric modeling and estimate; Modeling, estimation, prediction and testing in linear designs with dependent error structures using robust nonparametric methods, i.e. rank-based statistical modeling of hierarchical/nested designs;

Currently 

Developing Machine Learning (ML) course and Data Science materials; Expecting an external grant on a collaborative proposal in PSDS; Working with students in some research topics in PS and ML;

Classes

  • MATH 262: R/Applied Statistics

    An introduction to statistics with emphasis on applications. Topics include the description of data with numerical summaries and graphs, the production of data through sampling and experimental design, techniques of making inferences from data such as confidence intervals and hypothesis tests for both categorical and quantitative data. The course includes an introduction to computer analysis of data with a statistical computing package. (Those who have completed MATH 360 may not enroll in this course for credit, and no student may reveive credit for more than one 200-level statisticcs course, including credit for more than one of the following courses: BIOL 250, ECON 205, GEOG 278, MATH 242, MATH 262, PLSC 251, PSYC 250, and SOCL 211.) Offered every semester

  • MATH 361: Statistics

    Sampling distributions, point and interval estimation, and tests of hypothesis. Topics also include: regression and correlation, the analysis of variance, and nonparametric statistics. Prerequisites: MATH 360 or permission of the instructor. Offered every spring