Teaching and Curriculum

Teaching has always been an anchor in my work—an opportunity to translate complex ideas into shared understanding and to help learners connect data, theory, and practice. Across research and education, I aim to make learning environments transparent, inclusive, and evidence-based. My teaching integrates concepts from artificial intelligence, neuroscience, and data science with the human elements of reasoning, feedback, and curiosity.

Current Roles at Harvard Medical School

Curriculum and Research Fellow, Department of Biomedical Informatics (HMS)
As part of the AI and Machine Learning in Medicine (AIM) PhD program, I contribute to the design and delivery of several courses that bridge foundational AI methods with clinical and educational applications. My work involves developing learning materials, designing assessments, writing rubrics, and building analytic tools that help instructors and students understand their progress more clearly.

AIM2 — AI in Medicine

A course led by Marinka Žitnik, Ph.D.
AIM2 is an interdisciplinary course introducing students to the core methods of artificial intelligence in healthcare, from data integration and representation learning to ethical deployment. I support the development of open-source teaching materials, contribute to interactive notebooks and tutorials, and help align the computational exercises with pedagogical principles that emphasize interpretation, reproducibility, and collaboration.

BMIF204 — Foundations of Clinical Data

This course introduces graduate students to the structure and complexity of real-world clinical data, including EHR systems, clinical ontologies, and regulatory frameworks. My contributions include designing assignments that highlight the translation between data representation and clinical meaning, helping students appreciate the nuances of data quality, bias, and generalizability. The course is currently in development for expanded delivery through the Biomedical Informatics training programs at HMS.

In Development: BMI712 — AI in Medical Imaging

BMI712 will introduce principles of medical imaging data science, focusing on computational and ethical aspects of AI-based diagnostic tools. Drawing from my Ph.D. work in medical biophysics and breast MRI analysis, this course is being developed to combine imaging foundations with interpretability, clinical validation, and critical thinking around algorithmic performance. It will train students to evaluate model behavior and reliability in high-stakes clinical settings.

Previous Teaching Experience

Before joining Harvard Medical School, I served as a Teaching Assistant in several undergraduate and graduate courses at the University of Toronto and York University.

• At the University of Toronto, I supported courses in Statistics for Life Sciences (STA288), leading tutorials, coordinating assessments, and guiding students in quantitative reasoning and data interpretation.
• At York University, I assisted in Integral Calculus and Applied Statistics, where I emphasized conceptual problem solving and the translation of abstract mathematics into real-world insight.

These experiences helped shape my philosophy of teaching: that clarity, curiosity, and humility are the foundations of scientific learning.

Teaching Philosophy

Across all levels, I teach with the belief that learning is a collaborative experiment. My role as an educator is to design environments where students can test ideas safely, receive meaningful feedback, and build confidence in their own analytical voice. Whether developing AI systems or teaching about them, I aim to create structures that support deep understanding and enduring curiosity.