Azma
Software engineer & M.Ed. candidate
I build the systems that make learning work, and study why they should. My work sits at the intersection of software engineering, learning science, and learning analytics — designing technology-enhanced learning environments grounded in assessment and in how people actually learn.
Three lenses, one practice
Large-scale data systems
Production engineering on Microsoft's Dynamics 365 Customer Insights (Audience Insights) team and at Knowledgehook — event processing, APIs, and platforms built to run at real scale.
How people actually learn
M.Ed. candidate at OISE, University of Toronto, studying cognition, metacognition, and assessment design — the research base that should sit underneath every EdTech decision.
Instrumenting learning
Interested in what learner data — game telemetry, spaced-repetition performance, assessment traces — can reveal about understanding, and how AI can act on it responsibly.
Why this intersection
My interest in learning grew through studying computer science and mathematics. As I learned to work with abstraction, problem-solving, and feedback, I became interested not only in what I could build, but also in how people develop understanding. That curiosity eventually led me to graduate study in education. Most EdTech is built with deeper expertise in either engineering or learning. I have worked on both sides: building large-scale production systems and studying how instructional and assessment design shape learning. As advances in AI make more adaptive and responsive learning experiences possible, I want to help ensure they are grounded in both sound engineering and sound pedagogy. The most compelling problems lie at that intersection: translating models of learning into product and system decisions, then using evidence from learners to improve both.