Teaching at K-State and Influence of My Teaching on Students

       With 11 years of teaching experience at Kansas State University (K-State) I am a committed professional teacher in both classroom teaching and student advising. I have taught a broad spectrum of statistics courses at various levels that cover theoretical, computational and applied aspects. Overall, I have taught 42 classes with topics covering 17 different courses. Armed with my rich teaching experience and collaboration activities with research investigators and graduate students of different backgrounds, I am a competent, versatile instructor on all statistical courses offered at Kstate including applied, theoretical, and computational statistics at both undergraduate and graduate levels.

      Throughout my teaching at K-State, I have worked with a diverse audience, some of whom are Ph.D. students with strong mathematical and statistical foundation, while some are students or researchers from other disciplines that can recognize very few statistical terms. Regardless of their background and level, one of my aspirations is to share my knowledge of statistics with others in a way that positively impacts their learning experience or research experience. I hope that my students and colleagues can develop a deeper appreciation or passion of statistics through their interactions with me. I use the following principles to achieve this objective:  (1) teaching at the level of the audience; (2) engaging the students to learning by adding contents step by step and stimulating students through illustrative examples; (3) integrating the latest developments in statistical science into the curriculum; (4) providing a positive learning environment and encouraging critical and creative thinking; (5) encouraging students to work hard and develop positive learning attitude.  

       I have applied these principles in my teaching and advising and have received positive feedbacks from my students. In my advising, I pass my enthusiasm of statistical research and education on to my own students through their interaction with me during advising. Over the period that they work on their M.S. or Ph.D. research with me, they develop their own competence and passion toward statistical research and applications in applied fields, particularly in medical sciences. How well their enthusiasm has been nurtured can be seen by their career choice as well as their ability to get hired and thrive with their career choice. Here I will give a couple of examples.

         Dr. Ke Zhang, a former Ph.D. student graduated in 2008, was offered a job at Abbott Laboratories to conduct statistical research in Phase I clinical trial more than a year before his graduation.  After graduation, he continued to work at Abbott for a year and then accepted a tenure track Assistant Professor position at School of Medicine and Health Sciences in University of North Dakota, where he served as the Director of ND INBRE Bioinformatics Core. Lately, he was promoted to Associate Professor with tenure. After Dr. Zhang left Abbott (current AbbVie), every year AbbVie recruited one student from K-State with my recommendation, either for full time position or internship. Most other Ph.D. students of mine also found their enthusiasm in promoting education and scholarship and intellectual growth of statistics upon their graduation: George von Borries became an Assistant Professor. at Univ. of Brasilia; Siti Tolos accepted an Assistant Professor position at International Islamic Univ. of Malaysia; Mohammed Gharaibeh will be an Assistant Professor at Al Al-Bayt University in Jordan.

       My passion and competence in teaching and research of Statistics not only are embedded into my Ph.D. students, but also are passed along to my M.S. students. One example is Lei Dong, who wrote a M.S. report with me on a topic to numerically compare several low-dimensional and high-dimensional tests that are applicable to longitudinal data when the experiment contains a large number of treatments or experimental conditions. Upon graduation, he was hired by University of Kansas Medical Center to provide statistical analysis support for medical researchers. He is now a Senior Research Analyst in Department of Biostatistics, and the Data Manager for Office of Scholarly, Academic & Research Mentoring (OSARM), Department of Internal Medicine, University of Kansas Medical Center. A second example is about my former M.S. student Sharad Silwal. Upon graduation with M.S. in statistics and Ph.D. in mathematics, Sharad received two offers of faculty position. For both positions, it was his research and publication with me on statistical image analysis and his ability to teach statistics courses that placed him on top of other candidates. Santosh Ghimire, another former student who wrote a M.S. report with me on image segmentation, is now an Assistant professor of Tribhuvan University, which is the best university in Nepal. These examples not only speak about the quality of my teaching and advising, but also reflect our research directions.

 

Courses Taught at K-State:

Undergraduate level courses:

Stat 490:    Statistics for Engineering I (Spring 2007, Fall 2007, Fall 2011, Spring 2013, Fall 2014, Spring 2015)

Stat 510:   Introductory Probability and Statistics I (Fall 2004)

Graduate level courses:

Stat 702:   Statistical Methods for the Social Sciences (Spring 2005, Spring 2006, Spring 2010)

Stat 703:  Statistical Methods for Natural Scientists (Fall 2009, Fall 2010, Spring 2013, Summer 2014)

Stat 706:   Basic Elements of Statistical Theory (Fall 2004, Fall 2005, Fall 2006)

Stat 716:   Nonparametric Statistics (Fall 2011, Fall 2013)

Stat 726:   Introduction to Splus/R computing (Spring 2009)

Stat 745:   Statistical Graphics (Spring 2006 (2nd half), Spring 2008, Spring 2010, Spring 2012)

Stat 770:   Theory of Statistics (Fall 2006, Fall 2008, Fall 2013)

Stat 825:   Numerical Methods in Statistics (Spring 2011, Spring 2015)

Stat 902:   Generalized Lineal Models (Spring 2005, Spring 2007, Spring 2009)

Stat 903:   Spatial and Longitudinal Data Analysis (Fall 2005, Fall 2007, Fall 2009, Spring 2011)

Stat 904:   Resampling methods (Spring 2014)

Stat 940:   Advanced Statistical Methods (Fall 2014)

Stat 950:   Data Mining (Spring 2006, Fall 2010)

Stat 950:   Special topics on hypothesis testing in high-dimensional data (Spring 2005)

Stat 981:   Advanced Inference (Spring 2012)