Teaching
Instructor Experience
ISE 4223: Engineering Economics
University of Oklahoma, Spring 2026
As a member of the INFORMS student chapter at OU, I hosted seminars and tutorials on machine learning and research tools. The notebooks below provide practical examples, explanations, and resources for continued learning.
Supervised Machine Learning Tutorial
This notebook goes over the basics of machine learning, with a special focus on supervised machine learning. I demonstrate how to use python load and manipulate data, graph the results, and perform basic regression and classification. Also included are a variety of links to other websites, books, and blogs that students can use to continue learning.
You can access the notebook here.
Unsupervised Machine Learning Tutorial
Another branch of machine learning is unsupervised machine learning, in which the models learn about the data without any labels on the data. This can consist of finding unknown patterns in the data, outlier analysis, and dimensionality reduction.
You can access the notebook here.
Interpretability Tutorial
In this notebook, I cover the fundamental concepts of interpretability and explore various techniques including local, global, model-intrinsic, and model-agnostic methods. The tutorial dives into practical tools like Permutation Importance, LIME, SHAP, and Partial Dependence Plots, offering insights into how to interpret machine learning models and make them more transparent..
You can access the notebook here.
Research Mentoring
I have mentored master's and undergraduate researchers working on maternal-health prediction, cardiovascular risk stratification, childhood cancer survivorship, and large-scale clinical data preparation.
- Master's thesis research: Leama Tah, Abrar Mohamed, Caxton Muchono, Mel Benito, and Mahita Mallapu (2024–2025).
- Undergraduate research: Faith Morrison (Spring 2026) and Deaveyan Beltran-Hodge (Fall 2023).