Responsible AI Leadership & Community Engagement
Alongside my academic research on human–AI interaction, I’m committed to building structures that prepare students and professionals for the ethical use of artificial intelligence. Through public programming, cross-sector collaborations, and student-centered initiatives, I work to bridge the gap between research and responsible AI in practice. By designing public-facing programs and tools outside the lab, I aim to make these insights actionable—contributing to a broader culture of AI responsibility that’s inclusive, backed by science, and future-conscious.
Making AI research insights accessible for all
Princeton Laboratory for Artificial Intelligence | Fall 2025 - Spring 2026
As a part-time science communications fellow at Princeton's AI Lab, I create blog posts and social media content that bring cutting-edge AI research to a wider audience. I work with researchers, students, and Hub partners to highlight advances in areas such as human–AI interaction, interpretability, and other current cutting edge AI domains. At the core of this role is a belief that science should be accessible to everyone, so I aim to translate technical work into clear, engaging stories that connect research to its ethical and societal implications, strengthening public understanding and engagement.
I'm currently working as the lead author on a public-facing blog series focused on “machine understanding.” I'll incorporate insights from philosophy, neuroscience, and computer science to explore topics like:
What exactly do we mean when we say a system “understands” (applied to humans and machines)?
Why it's important for everyone (e.g. researchers, policy makers, and the public) to be informed about machine understanding.
How human intuitions about machine understanding may affect our interactions with AI.
Current approaches that ML researchers use to assess for machine understanding.
Intersections of ML and human neuroscience research.
It is scheduled to be released in the spring of 2026. Check back for updates!
Partnering with the Provost’s Office to build a student-facing Responsible AI ecosystem
New Jersey AI Hub | Spring–Summer 2025
As a part-time university administrative fellow at New Jersey’s AI Hub--a cross-institutional initiative connecting researchers, educators, and policymakers across New Jersey to advance interdisciplinary research, student training, public engagement, and workforce development--I help shape the university’s efforts to engage students with AI. This included co-facilitating major events like the AI Innovations Networking & Mingle Room at Reunions, hosted with the Center for Innovation. I also collaborated with university leadership and faculty to develop resources and opportunities that prepare students for ethical AI careers.
I continued this work through Summer 2025 with a focus on workforce readiness. I co-authored a book chapter exploring the Hub's strategy to address region-specific workforce needs. I helped to communicate preliminary results from a statewide survey study anticipating New Jersey’s AI workforce needs, led by the AI Hub in collaboration with the County College of Morris and Princeton’s Survey Research Center. The survey and focus groups capture employer perspectives on AI integration, skill demands, and credentialing preferences across key sectors such as life sciences, finance, and logistics. My role centered on communicating how the Hub plans to use emerging trends to inform programmatic decisions and help guide where the AI Hub should focus its strategic efforts.
Creating space for critical reflection on ethical AI across disciplines
University Administrative Fellow | Responsible AI Learning Cohort - Fall 2024
As a University Administrative Fellow, I organized and facilitated the Responsible AI Learning Cohort: a student-led, discussion-based series that brought together graduate students from across departments to explore topics in AI ethics, fairness, transparency, and governance. The cohort fostered interdisciplinary dialogue, helped students connect their technical work to real-world consequences, and served as a model for ethical AI training in higher education.
See a write-up I co-authored about the RAI Leaning Cohort here!
Designing tools to make explainability and LLMs more accessible
Social Impact Fellow | Responsible AI Institute - Summer 2024
At the Responsible AI Institute, I developed a practitioner-friendly guide on explainability and interpretability in large language models (LLMs). This resource was designed to support stakeholders in understanding how LLMs make decisions and what that means for transparency, accountability, and trust. The fellowship deepened my applied knowledge of responsible AI standards and frameworks and gave me experience translating complex research into actionable resources.