Research

Research & Projects

My long-term interest is in how engineering education and practice should evolve as the tools and systems around us become increasingly intelligent. I am particularly interested in how greater machine capability can translate into greater human capability: how engineers learn to work with increasingly capable tools, where human judgment and verification remain essential, and how engineering workflows and learning environments can be redesigned accordingly.

I explore these questions through engineering education, software engineering, and computing systems. My background in computing and software engineering practice shapes how I approach them: I am especially drawn to work that involves building, evaluating, and reasoning about real technical systems rather than studying technology use in isolation.

More broadly, I value collaborative work that brings together complementary perspectives, especially when it creates opportunities to explore new questions, build useful systems, and learn from different ways of approaching a problem.

Areas of Interest

Current Interests

Engineering Learning & Judgment

I study how course design can help engineers develop technical judgment, engage with authentic complexity, and build durable capabilities as tools and systems continue to evolve. I am particularly interested in how students learn to reason about technical choices, evaluate their work, and navigate increasingly complex engineering tasks.

Human-Tool Collaboration in Engineering

I am interested in how increasingly capable tools change engineering workflows, including what to delegate, what to verify, what to understand deeply, and how engineers can use these tools effectively while retaining judgment and responsibility for the resulting work.

Software & Intelligent Computing Systems

I am interested in software and computing systems that make effective use of increasingly capable tools and components. I am especially drawn to work involving system design, implementation, and evaluation, and to questions about how new capabilities can be incorporated into real systems in useful and reliable ways.

Current Work

Projects

Current work explores how increasingly capable tools can support engineering learning and practice without displacing the judgment, verification, and technical reasoning that remain central to good engineering.

AI Learning Companion for Graduate Engineering Education

January 2026 — present · Ongoing

An AI-supported learning environment that helps students explore authentic software systems and professionally developed codebases through guided, grounded explanations rather than generated solutions.

Supported by the Graduate Education Innovation Fund, University of Toronto

Learn more