Teaching

BMI 510: Biostatistics for Machine Learning

Semester: Spring

Offered: 2025

Co-taught with Dr J. Lucas McKay

This course presents an accelerated introduction to the concepts and methods of biostatistical data analysis suitable for applying machine learning approaches on clinical data. Topics include exploratory data analysis with grammar-based data visualization (e.g., ggplot2/seaborn); dimensionality reduction, measures of model fit, descriptive statistics and confidence intervals for categorical, ordinal, and continuous variables with normal and non-normal distributions; measures of association; statistical power; one- and two-sample hypothesis tests; ANOVA and hierarchical linear models

BMI 585: Topics in Biomedical Informatics: Navigating AI Ethics in Health

Semester: Fall

Offered: 2025

Co-taught with Dr Azra Ismail

This course offers an accessible, case-based introduction to the ethical dimensions of artificial intelligence in health and society. Each week, students will explore a real-world AI application through a different ethical lens, ranging from bias and fairness to privacy, accountability, and environmental impact. Designed for students with diverse backgrounds, the course emphasizes active discussion, collaborative projects, and intuitive understanding rather than technical depth. By the end of the course, students will be able to critically assess AI systems using practical frameworks like model cards and AI "nutrition labels," and develop thoughtful, ethically grounded critiques of emerging technologies.

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