Teaching
Teaching spans undergraduate, graduate, and professional education, bridging engineering, clinical medicine, and the humanities. Courses are designed to develop both technical competence and the critical judgment to evaluate how computational tools shape health and care.
Courses
Ancient Wisdom for Future Medicine (UGS 302)
A freshman seminar exploring how historical traditions—including Judeo-Christian, Buddhist, and Greek thought—inform contemporary debates in medicine and emerging technologies such as AI, gene editing, and brain-computer interfaces.
The course emphasizes discussion, reflection, and the development of frameworks for evaluating technological change.
Computational Neuroscience (Mathematical Physiology)
A graduate-level lecture series introducing engineering and computational science students to neurological systems and clinical applications.
Topics include neural dynamics, modeling approaches, and the use of computational tools to study disease and intervention.
Technology and Medicine (Medical Elective)
An elective course for medical students focused on artificial intelligence, data science, and emerging technologies in clinical care.
The course emphasizes data quality, model limitations, bias, and the role of clinicians in evaluating and shaping AI-enabled tools.
Mentorship and Training
Students, residents, and trainees across disciplines work with the lab to develop research questions, design studies, and apply quantitative methods to clinically meaningful problems.
As Associate Program Director for Quantitative Research in the Neurology Residency Program, Dr. Chang mentors residents in translating clinical observations into testable hypotheses, designing and analyzing research and quality improvement projects, and interpreting data within appropriate clinical and statistical frameworks. Trainees participate in all stages of research, including analysis, presentation, and publication.