Emelia Hughes is a PhD student at Notre Dame working in the area of Social Computing, mainly focusing on online communities, social media, and the spread of information online. Right now, she is working on projects related to algorithmically driven and event-based communities on TikTok. She currently teaches Generative AI for the ESTEEM Graduate Program.
Before starting at Notre Dame, Emelia studied at the University of Washington, where she earned a Bachelor of Science in Informatics and a Bachelor of Arts in Drawing & Painting.
Emelia shares some insight into her professional journey and offers advice to ESTEEM students.
ESTEEM: What was the most surprising or unconventional lesson you’ve learned in your professional journey?
Emelia: One surprising lesson has been how much my art background has helped my work in research and teaching. The most useful skill isn’t “having the perfect idea.” It’s iterating: trying something, getting feedback, and being willing to revise (sometimes a lot) without taking it personally.
ESTEEM: Can you share a pivotal moment in your career when you took a risk or embraced uncertainty, and what you learned from it?
Emelia: A pivotal risk was choosing the academia and research route rather than pursuing industry full-time. I was drawn to research because I wanted the freedom to ask deeper questions about how people experience technology and how algorithmic systems shape online communities. That path also comes with real uncertainty: projects shift over time, results can be surprising or messy, and the feedback cycle is long. What I learned is that the uncertainty isn’t a sign you’re doing it wrong. In many cases, it’s evidence you’re working on problems that are genuinely complex and worth studying. The key is to stay grounded in the people you’re trying to understand and keep letting evidence guide your next step.
ESTEEM: If you were a student starting the ESTEEM program this year, what would you make sure to focus on learning or doing during the year?
Emelia: I’m biased as the AI instructor, but I’d make sure to build a strong foundation in how AI systems work and where they fail. Not just “how to use them,” but what’s happening conceptually, what assumptions they make, and what kinds of mistakes they predictably produce. Alongside that, I’d prioritize learning how to communicate clearly about AI to both experts and non-experts, because that skill matters in almost every role. Finally, I’d make sure to build something end-to-end, even a small prototype, because you learn the most when you’re forced to make real decisions about data, evaluation, UX, and tradeoffs.
ESTEEM: What emerging trends or challenges in your field do you think ESTEEM students should be paying attention to?
Emelia: Two things stand out. First, trust and accountability as AI gets embedded into everyday products: how we evaluate quality, communicate limitations, prevent harm, and make systems legible to the people relying on them. Second, the shift toward AI-mediated online spaces, where algorithms shape what people see, believe, and how communities form. For students, the big challenge is learning to balance capability with responsibility. Strong technical work is important, but so is anticipating second-order effects, thinking about incentives, and designing for real-world contexts rather than ideal ones.
ESTEEM: What do you personally learn from working with ESTEEM students year after year?
Emelia: I learn constantly from ESTEEM students. Because all of the students come in with strong and diverse prior experience, they regularly surprise me with the approaches they take and the solutions they come up with. Seeing how they tackle the same problem from different angles pushes me to teach more flexibly and to keep rethinking my own assumptions.
ESTEEM: If you had one piece of advice for students striving to make an impact, what would it be?
Emelia: Start small, iterate fast, and stay close to the people you’re trying to serve. The students who make the biggest impact aren’t always the ones with the flashiest ideas. They’re the ones who keep refining, testing, and learning, and who can explain their choices clearly. I’d also add: be honest about what you don’t know yet. Curiosity, humility, and follow-through go a long way, especially when you’re building things that affect other people.