Teaching

My teaching focuses on preparing students and trainees to engage thoughtfully with the increasing role of data, computation, and technology in medicine.

Across undergraduate, graduate, and professional education, I aim to bridge engineering, clinical medicine, and the humanities—helping students develop both technical competence and the ability to critically evaluate how these tools shape human health and care.

Teaching Approach

I aim to form not only skilled analysts, but thoughtful interpreters of data and technology in clinical and societal contexts.

My teaching emphasizes:

  • Conceptual understanding over memorization
    Students learn underlying principles that generalize across problems and domains.

  • Connection to real-world problems
    Technical methods are grounded in clinically meaningful applications.

  • Interdisciplinary thinking
    Courses integrate perspectives from engineering, medicine, and the humanities.

  • Critical engagement with technology
    Students are encouraged to examine the assumptions, limitations, and implications of data-driven tools.

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

I work with students, residents, and trainees across disciplines 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, I mentor residents in: * Translating clinical observations into testable hypotheses
* Designing and analyzing research and quality improvement projects
* Interpreting data within appropriate clinical and statistical frameworks

Mentorship is an integral part of my work, with trainees participating in all stages of research, including analysis, presentation, and publication.

Additional Resources

  • Course syllabi available upon request
  • Selected materials and lectures may be shared in the future