Dr. Huang (Frederick) Lin knows firsthand how artificial intelligence (AI) can support advances in public health: he uses AI daily in his own research analyzing the trillions of microbes living in our guts, looking for those that contribute to major diseases such as inflammatory bowel disease, HIV/AIDS, and various cancers.
This semester, he’s sharing his learning exponentially. Lin has just launched the first ever machine-learning class in the UMD School of Public Health (SPH): Foundations of Machine Learning in Public Health (EPIB 674).
“Machine-learning classes are everywhere now, but I want to give students a specific perspective. Once they learn theoretical foundations, they look at how to adapt these methods specifically to public health projects, applying machine learning to analyze health-related data,” said Lin, who is assistant professor of biostatistics in SPH’s Epidemiology and Biostatistics Department.
Machine learning allows researchers to rapidly analyze large datasets, process complex data with multiple variables and create predictive models to inform real-world decision-making – processing far beyond what the human brain or traditional statistical methods can achieve.
Housed in SPH, the class is open to all UMD graduate students. It will be offered again in Spring 2026, after a pilot phase for evaluation, student feedback and fine-tuning. The class is embedded in one of SPH’s newest degrees, the doctorate in biostatistics.
Lin has planned the course to be accessible to all students, and especially to those who may be less expert in statistics, mathematics or computer science.
“It is critical for students, and especially public health students, to understand machine learning,” said Lin. “AI is developing very fast and, whether you like it or not, you have to have some knowledge. This class is an opportunity for non-statistics students to step into the field of machine learning.”
This class is an opportunity for non-statistics students to step into the field of machine learning.
Ria Warrier, graduating this spring with an MPH, with a concentration in epidemiology, said entering the job market with knowledge of AI and public health will help her stand out. She also appreciates Lin’s approach.
“I've never taken a course like this!” she said. “I’ve never touched on machine learning nor have I taken an intensive computer science class. Professor Lin’s class was easier for me to digest because the material was taught in a public health framework.”
Rong Pan, a first-year biostatistics PhD student, is excited to gain the skills she needs to analyze huge datasets relating to her subject area: bioinformatics. “My research is mainly focused on developing statistical and computational methods for genetics and epigenetics data, and so it is clearly linked to AI. And in this class, I get to learn hands-on coding using AI,” said Pan.
Lin believes this new class will prepare students as they head into careers in public health, where employers are often on the lookout for machine-learning experience.
“I hope this course will benefit students beyond those studying biostatistics. And I also would love in the future to see us offer more advanced machine-learning courses in public health, for example, deep learning in health and biomedicine and AI for health equity and ethics.