Juliette Woodrow

My older sister, Isabelle, inspired me to explore computer science. She’s five years older than me and also studied CS at Stanford. I loved the way she described her projects as puzzles and creative challenges to figure out. We grew up solving puzzles, riddles, and challenges together. Her excitement rubbed off on me, and I couldn’t wait to try it myself! 

When I took my first computer science course at Stanford, CS106A, I was hooked. I loved the creativity and challenge of building something and finally making it work. Soon after, I began teaching for that same class through Stanford’s CS198 program, which lets undergraduates become near-peer teachers after just a few introductory courses. As a “section leader,” I led weekly small-group sessions for about 10 students. This model is one of the things that makes Stanford’s CS program special. It gives new learners personal support while helping section leaders deepen their own understanding through teaching. CS106A is still one of my favorite parts of Stanford. It’s a course full of discovery: Students go from never having written a line of code to creating interactive games or data-driven projects in just a few weeks. Helping students realize that computer science is something they can do is my favorite part of teaching.

Now I’m in my fourth year of the PhD program in computer science, and I have carried that love of teaching into my research. One of the projects I’m most involved with is Code in Place, a global initiative that brings Stanford’s introductory computer science course to learners around the world. Each year, more than 10,000 students and 1,000 volunteer teachers come together for six weeks of learning and mentorship. The program mirrors Stanford’s peer-teaching model, connecting small groups of students with volunteers who guide them through the same material taught in CS106A.

Together with Ali Malik, I helped build TeachNow, a system that supports Code in Place’s volunteer teachers and enables them to teach students on demand. The goal was to make it easier for volunteers to focus on the human side of teaching (connecting, encouraging, and explaining) while technology handles the logistics.

My research connects directly to this work. I build AI-powered tools to make teaching and learning more effective, especially in large-scale, human-centered learning environments like Code in Place. A big part of my focus is making these algorithms interpretable so that teachers can see why the system is making a particular suggestion. Most machine learning systems make predictions without explaining their reasoning. In education, that isn’t enough. Teachers need to understand how a model reached its conclusion in order to trust and use it. I want to build systems that surface meaningful insights, save teachers time, and help students feel more seen and supported in their learning.

At the core, my work aims to amplify what teachers already do well. Great teachers notice patterns: who is struggling, how are students approaching a problem, when do they need encouragement. If we can design AI tools that make those patterns visible earlier and more clearly, we can make teaching and learning more effective while keeping the human connection at the center.

My experience here has felt really full circle. I’m proud to follow in my older sister’s footsteps and to have our younger sister, who is an undergraduate here at Stanford, follow in mine. I watched her learn computer science from the ground up and then go on to even be a section leader for Code in Place.

I’m really close with my family. My sisters and I are in a monthly book club with our grandmother, Joan, who’s a huge reader and always has the best book recommendations. She inspired my love of reading, which has become one of my favorite ways to keep learning. I especially love historical fiction that dives into a topic or era I know nothing about. Those stories send me down a deeper rabbit hole of discovery. That same love of learning through stories shapes how I teach. I’m always looking for ways to help students connect new ideas to something familiar, through analogies, examples, or small narratives that make a concept click. Watching those moments of understanding, when students start to believe in themselves, is what keeps me coming back to teaching and research.