teaching portfolio

My path into teaching has developed alongside my work in human-computer interaction and my interest in how people think, communicate, and work together in complex settings. During my PhD at Cornell University, I’ve been a teaching assistant for courses in machine learning, virtual and augmented reality, and computing for global development. These experiences have shaped how I think about teaching, not as delivering content, but as helping students build understanding together.

A lot of my approach is influenced by my research on shared cognition and teamwork in high-stakes environments. In those settings, small misunderstandings can have big consequences, and I see a similar dynamic in the classroom. Because of that, I focus on making learning collaborative and grounded. I try to connect abstract concepts to real-world applications, drawing on my experience building systems like MazeWorld to study human–AI teaming, and encourage students to think about how what they’re learning applies in practice.

In my teaching, I emphasize active learning and problem solving. I structure sessions around questions and scenarios, often having students work in small groups before discussing ideas as a class. This helps students engage more deeply and build confidence in their own thinking. At the same time, I encourage them to think critically about the systems they design, including questions of usability, accessibility, and broader impact.

Ultimately, my goal is to create an environment where students feel comfortable engaging with difficult ideas, asking questions, and learning from each other. I want them to leave with both strong technical skills and the ability to think critically about technology, so they are prepared to navigate complex problems in research, industry, and beyond.