I am a researcher and educator who believes that learning and discovery are most powerful when they remain grounded in the human experience. My work sits at the intersection of artificial intelligence, education, and health, where I study how intelligent systems can support—not replace—expert judgment, pedagogy, and care. Across projects, my focus is on building AI that deepens understanding, strengthens trust, and makes complex processes more visible rather than more opaque.

My academic path has moved through several disciplines that continue to shape how I think about knowledge, responsibility, and impact. I began in radiation therapy, where precision, accountability, and patient trust are non-negotiable. That foundation expanded through training in physics, mathematics, and statistics, where I developed a respect for both the power and fragility of models built on complex data. These experiences ultimately led me to a Ph.D. in Medical Biophysics at the University of Toronto, where my research focused on AI for breast cancer risk prediction. There, I developed methods integrating medical imaging and natural language processing to model longitudinal patient risk, and learned firsthand that meaningful innovation in healthcare AI depends not just on performance, but on interpretability, robustness, and clinical fit.

Today, as a Curriculum and Research Fellow in the Department of Biomedical Informatics at Harvard Medical School, my work centers on AI for graduate and medical education. I study how learners acquire, retain, and apply knowledge over time, and how computational models can be designed to reflect those processes more faithfully. Drawing on insights from learning science and neuroscience, I am particularly interested in memory-aware and adaptive learning systems that personalize feedback, surface conceptual structure, and support reflective practice for both students and instructors.

Alongside this research, I work on scalable educational infrastructure that enables responsible experimentation with AI in real courses. This includes systems for automated short-answer assessment, generative feedback aligned to instructional goals, and course-level learning analytics that help faculty see patterns in student learning without losing the nuance of individual trajectories. Across these efforts, I emphasize transparency, bias awareness, and evaluation frameworks that prioritize trustworthiness and pedagogical value over novelty alone.

What ties this work together is a commitment to shared curiosity and iterative improvement. The best learning environments—like the best AI systems—are collaborative, adaptive, and accountable to the people they serve. I see my role as building bridges between the technical and the human: between how machines represent knowledge and how people create meaning, learn, and make decisions in complex, real-world settings.

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)