Dr. Gaynanova’s teaching methods emphasize reproducible research practices, statistical computing and communication skills; her goal being to prepare students for STEM-oriented careers. Her teaching has been recognized with a Cornelia Ye Outstanding Teaching Assistant Award at Cornell University and she served as a faculty mentor in the Academy for Future Faculty at Texas A&M University.
Classes taught at Texas A&M University
STAT 211: Principles of Statistics I. (Fall 2015, 2016; Spring 2016, 2017) Calculus-based introduction to probability and probability distributions; sampling and descriptive measures; inference and hypothesis testing; analysis of variance; linear regression.
STAT 489: Structured Research Experience in Statistics (Summer 2021, Spring 2022) Research project based on simulation studies in statistics; version control; reproducible computations; simulation study design; publication-quality figures; scientific writing.
STAT 610: Distribution Theory. (Fall 2017, 2018, 2019, 2020, 2021, 2022) Graduate-level introduction to probability theory; distributions and expectations of random variables, transformations of random variables and order statistics; generating functions and basic limit concepts.
STAT 689/STAT 695: Statistical Learning with Sparsity. (Spring 2017, 2020) Graduate-level class covering penalized empirical loss minimization methods with sparsity-inducing penalties. The course also includes brief introduction to convex optimization and duality.
STAT 600 (former STAT 689): Statistical Computing. (Spring 2019; Fall 2019, 2020, 2021, 2022) Graduate-level course on computational statistics and optimization. Topics include version control with Git and Github, code vectorization and profiling, writing R packages, introduction to convex optimization and optimization algorithms.
Classes taught at Cornell University
STSCI 2100/ILRST 2100: Introductory Statistics. (Winter 2013, 2014, 2016) Introduction to descriptive statistics and basic statistical methods, no calculus background is required.