Teaching

Teaching is great fun and hard work for me. As an associate professor at the University of Missouri, I have taught  multiple courses at the Ph.D., M.S., and undergraduate levels. At University of Missouri, my teaching responsibility is three courses per academic year. I find teaching refreshing and enhancing to my own knowledge as I try new approaches and continuously revising current methods to guide the students more effectively. I find effective ways to challenge my students to achieve their full potential and encourage them to learn the fundamental concepts thoroughly. In my class lectures, I put a lot of emphasis on the common intuition behind any statistical concept, how to use it for analyzing data sets, how to interpret an outcome, and finally how to draw conclusions from the analysis. My deepest satisfaction lies in seeing the best students excel, seeing the pride of average students in completing a term project that is longer, more challenging, and better crafted than they thought possible, and seeing the satisfaction of less-prepared students persevering when they thought they couldn’t make it.

To some extent, I have been able to develop my own teaching skills based on my experiences as a student and as an associate professor. I remember the instructors I liked as a graduate student and the things about the classes they taught that made them positive learning experiences. As an associate professor, I have observed in my class that some types of lecturing styles make the material interesting, while some put the whole class to sleep. Some alternatives to lecture, such as small-group discussions, labs, and question-and-answer sessions, work well; others have flopped. Some types of exams really test how much students have learned; others simply seem to test how fast they can process information or how adept they are at spotting the pitfalls and trickeries. I design assignments to reinforce the understanding of material. From all of these things, I have learned what makes for a positive, beneficial learning experience, and I continuously incorporate them into the development of my own teaching style.

In Fall 2025 I am teaching STAT 4760/7760: Statistical Inference.

List of Courses I teach/taught at University of Missouri

• STAT 4110/7110: Statistical Software and Data Analysis. Level: Graduate/Advanced Undergraduate Programming with major statistical packages like SAS and R. Emphasizing data management techniques and statistical analysis for regression, analysis of variance, categorical data, descriptive statistics, non-parametric analysis, and other selected topics. Sample Syllabus.

• STAT 4510/7510: Applied Statistical Models I. Level: Graduate/Advanced Undergraduate Textbook: Introduction to Statistical Learning with Applications, by  Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani. Introduction to applied statistical models including regression and ANOVA, logistic regression, discriminant analysis, tree-based methods, semi-parametric regression, support vector machines, and unsupervised learning through principal component and clustering.  Sample Syllabus.

• STAT 4580/7580: Introduction to Statistical Methods for Customized Pricing. Level: Graduate/Advanced Undergraduate. Introduction to basic concepts of and statistical methods used in customized pricing. Focuses on applying statistical methods to real customized pricing problems. Students will gain an understanding of customized pricing and some hands on experience with SAS Enterprise minor. Sample Syllabus.

• STAT 4710/7710: Introduction to Mathematical Statistics. Level: Graduate/Undergraduate Textbook: Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, by J. Susan Milton and Jesse C. Arnold.Introduction to theory of probability and statistics using concepts and methods of calculus. Sample Syllabus.

• STAT 4640/7640: Introduction to Bayesian Data Analysis. Level: Graduate/Advanced Undergraduate Textbook: 1) Bayesian Statistical Modelling by Peter Congdon (Second Edition, Wiley). 2) Bayesian Modeling Using WinBUGS (Wiley Series in Computational Statistics) by Ioannis Ntzoufras. Bayes formulas, choices of prior, empirical Bayesian methods, hierarchial Bayesian methods, statistical computation, Bayesian estimation, model selection, predictive analysis, applications, Bayesian software. Sample Syllabus.

• STAT 4750/7750: Introduction to Probability Theory. Level: Graduate/Advanced Undergraduate. Textbook: Introduction to Mathematical Statistics, 6thEd., Hogg, McKean & Craig. Chapters 1 to 6. Probability spaces; random variables and their distributions; repeated trials; probability limit theorems.

• STAT 4760/7760: Statistical Inference. Level: Graduate/Advanced Undergraduate Textbook: Introduction to Mathematical Statistics, 6th Ed., Hogg, McKean & Craig. Chapters 6 to 9. Sampling; point estimation; sampling distribution; tests of hypotheses; regression and linear hypotheses. Sample Syllabus.

• STAT 8220: Advanced applied linear models including mixed linear mixed models (fixed and random effects, variance components, correlated errors, split-plot designs, repeated measures, heterogeneous variance), generalized linear models (logistic and Poisson regression), nonlinear regression. Sample Syllabus.

• STAT 8410: Statistical Theory of Bioinformatics. Level: Graduate Textbook: Statistical Methods in Bioinformatics by Warren J. Ewens, Gregory. Study of statistical theory and methods underpinning bioinformatics. Topics include statistical theory used in biotechnologies such as gene sequencing, gene alignments, microarrays, phylogentic trees, evolutionary models, proteomics and imaging. Sample Syllabus.

• STAT 8640: Bayesian Analysis I. Level: Graduate Textbook: Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki  Vehtari,  and  Donald  Rubin,  3rd  edition.Bayes' theorem, subjective probability, non-informative priors, conjugate prior, asymptotic properties, model selection, computation, hierarchical models, hypothesis testing, inference, predication, applications. Sample Syllabus. 

• STAT 9530: Data Mining and Machine Learning Methods. Level: Graduate (Ph.D) Textbook: Hastie, Tibshirani, and Friedman, The elements of statistical learning, 2nd edition. Approaches to estimating unspecified relationships and findings unexpected patterns in high dimensional data. Computationally intensive methods including splines, classifications, tree-based and bagging methods, support vector machines. Sample Syllabus.

List of Day Long Short Courses I have taught

• Spring 2024: Conducted a half day short course on “Machine Learning & Cloud Computing with R,”  on April 09, 2024 at ARD Research Symposium 2024 at Nashville, TN. The Association of 1890 Research Directors (ARD) is the federation of 19 autonomous Historically Black College and University (HBCU) 1890 land grant universities and this symposium is their biggest bi-annual event. The short course was open for all without any cost. Overall, around 100 students, staff, faculty, and other professionals attended the short course.

•  Spring 2024: Offered a day long online short course on “Power BI and SQL,”  on March 16, 2024 at University of Missouri. The short course was open for all with a nominal registration fee. Overall, around 70 students, staff, faculty, and other professionals attended the short course.

•  Spring 2019: Offered a day long online short course on “Introduction to Data Science with R,” on Mach 27, 2019 at National University of Life and Environmental Sciences of Ukraine, Kiev, Ukraine. The short course was open for all students completely free of cost. Overall, around 50 students attended the short course.

• Fall 2020: Offered a day long online short course on “Introduction to Cloud Computing with R in Google Cloud and Amazon Web Services,”  on Sept 19, 2020 at University of Missouri. The short course was open for all with a nominal registration fee. Overall, around 150 students attended the short course.

•  Spring 2019: Offered a day long online short course on “Introduction to Data Science with R,” on Mach 27, 2019 at National University of Life and Environmental Sciences of Ukraine, Kiev, Ukraine. The short course was open for all students completely free of cost. Overall, around 50 students attended the short course.