I am a researcher and educator who believes that learning and discovery are most powerful when they stay connected to the human experience. My work sits at the intersection of artificial intelligence, education, and health — exploring how we can use intelligent systems not to replace expertise or empathy, but to deepen them.
My academic path has taken me through several disciplines that continue to shape how I think about knowledge and care. I began in radiation therapy, where I learned the value of precision and trust in clinical practice. That foundation grew through studies in physics, mathematics, and statistics, where I came to appreciate the beauty and responsibility of working with complex data. These experiences ultimately led me to a Ph.D. in Medical Biophysics at the University of Toronto, where I focused on AI for breast cancer risk prediction, integrating natural language processing and imaging to better understand patient outcomes. That project taught me that meaningful innovation happens when algorithms are designed not only to perform, but to explain — when they serve both scientific progress and human understanding.
Today, as a Curriculum and Research Fellow in the Department of Biomedical Informatics at Harvard Medical School, I focus on building AI tools for graduate and medical education that are rooted in pedagogy and neuroscience. My work explores how learners acquire and retain knowledge, and how computational models can help us observe and support that process more effectively. I’m especially interested in adaptive learning systems that personalize feedback, visualize conceptual growth, and encourage reflective practice among students and instructors.
I also work on scalable educational infrastructure — building the technical and conceptual frameworks that allow instructors to experiment responsibly with AI in their teaching. This includes projects on automated short-answer evaluation, generative feedback systems, and course-level learning analytics that empower faculty to see patterns without losing the nuance of individual learners. My approach emphasizes transparency, bias awareness, and the importance of feedback that is interpretable and trustworthy.
What ties all of this together is a belief that education and research thrive on shared curiosity. The best learning environments, like the best algorithms, are collaborative and self-improving. I see my role as helping to build bridges between the technical and the human — between how machines represent knowledge and how people create meaning from it.
Education
- Ph.D., Medical Biophysics — University of Toronto (2017–2023)
- M.A., Applied Mathematics & Statistics — York University (2016–2017)
- B.Sc., Physics (Minor in Math & Stats) — University of New Brunswick (2013–2016)
- B.H.Sc., Radiation Therapy — University of New Brunswick (2009–2013)