Research

My work explores how to bring AI and cognitive-science insights into real-world education and health settings. At its core, I believe artificial intelligence should help people learn, not just automate tasks, by making the invisible processes of learning and decision-making visible and actionable.

Current Projects

Exercise Recommendation and Student Learning Modeling
Drawing from neuroscience and cognitive psychology, I am developing a system that helps learners choose high-impact practice activities and tracks how their understanding evolves over time. This work combines insights from memory, attention, and metacognition to create adaptive learning models that strengthen long-term retention and conceptual integration. The goal is to make the science of learning tangible for both students and educators.

Automated Short-Answer Grading and Learning-Outcomes Analysis
Using large language models and natural language processing, I build tools that analyze short-answer responses, assess conceptual understanding, and support evidence-based instruction. Each system is grounded in clear rubrics and transparency checks, ensuring that AI feedback remains interpretable, fair, and pedagogically sound. The focus is not on automation for its own sake, but on deepening reflection and closing the loop between learning objectives and outcomes.

Flipped-Classroom Chatbot Support
I am developing a conversational AI assistant that supports flipped-classroom environments. The chatbot engages students before class, prompting reflection on readings and identifying areas of uncertainty. Instructors receive summaries of key themes and misconceptions, enabling them to tailor class time toward synthesis and reasoning. The aim is to make pre-class preparation more interactive and inclusive while giving educators insight into the evolving thought processes of their learners.

Faculty Support for Learning-Focused Syllabus Design
Faculty play a central role in shaping how AI enters the classroom. I am building a toolkit that helps instructors design courses, assessments, and feedback strategies grounded in cognitive science and data-informed rubrics. This initiative focuses on responsible and accessible AI integration, supporting instructors who wish to bring evidence-based methods into their teaching without needing to become technical specialists.

Health and Imaging AI

Alongside my educational work, I continue research in biomedical imaging and clinical risk modeling. My doctoral research in Medical Biophysics at the University of Toronto focused on AI for breast MRI interpretation and risk prediction, integrating imaging and text data to capture subtle markers of disease. I have also worked on explainable segmentation, language understanding in radiology reports, and mixed-effects modeling to quantify variability in imaging biomarkers. These experiences inform my educational research—reminding me that transparency, fairness, and interpretability are vital whether the data represent patients or students.

Overarching Themes

1. Transparency and Explainability
   Whether in clinical research or education, tools must make sense to their users. I design models where reasoning and results can be examined, questioned, and trusted.

2. Learner-Centered Design
   I focus on the cognitive journey of the learner—what they know, what they are ready to explore next, and how their understanding changes through feedback and reflection.

3. Scalable and Responsible Infrastructure
   I aim to build systems that scale thoughtfully. AI should amplify human expertise, not flatten it. My goal is infrastructure that supports growth, curiosity, and accountability across disciplines.