Hi! I’m a recent UC Berkeley graduate from the island of Saipan in the Northern Mariana Islands, and I studied Computer Science and Data Science with an emphasis in cognition.
This upcoming fall, I’ll be attending the Baskin School of Engineering at UC Santa Cruz for my Master’s in Computer Science, with a research focus in equitable computing education under Professor Hao Yue.
I’m passionate about finding ways to make computing and data science education more equitable and accessible. I discovered my passion for computing education through my position as an undergraduate teaching assistant for Data 8: Foundations of Data Science (UC Berkeley’s largest course!), teaching weekly discussion sections across core data science topics using Python and Jupyter notebooks. I’m currently spending my final summer in Berkeley as a Head Teaching Assistant for the course.
I also organize as a proud member of UAW Local 4811, the union of 48,000 academic workers at the University of California. In UC Berkeley EECS and Data Science, a supermajority of academic workers are undergrads—but it’s only because of member organizing that we’ve won strong workplace protections in order to maintain the quality of instruction that students deserve.
I’ve been a teaching assistant for lower-division data science courses at UC Berkeley since Summer 2024, the summer after my sophomore year. I’m passionate about expanding access to the computing classroom, and practicing inclusion through pedagogical practices and culturally responsive curriculum. I believe inclusion means more than just helping students feel comfortable in the classroom—but finding ways to ensure that they recognize they truly belong in the field.
Most recently, I was the head teaching assistant for Data 6: Introduction to Computational Thinking with Data, funded through an NSF Improving Undergraduate STEM Education (IUSE) grant. The course was designed primarily for social students and emphasizes rigorous computational problem-solving to prepare students for computational social science research and advanced computer science coursework.
Before then, I was a teaching assistant for Data 8: Foundations of Data Science, which teaches core data science concepts across statistical inference, data analysis, and computational thinking through Python and the pandas-based datascience library. Data 8 is the first course where I felt capable of success as a programmer, and I’m grateful to have had the privilege of teaching hundreds of students in my discussion sections and helping them recognize their potential and ultimately see themselves as data scientists.
I make all my teaching materials public! Here are my teaching materials from the Summer 2025 iteration of Data 8.
Through my experiences in teaching and curriculum development, I’ve developed a strong interest in computer science education research—specifically in designing curriculum, pedagogical approaches, and educational tools that expand access to computing for nontraditional and underrepresented students while fostering deep conceptual understanding and a genuine sense of belonging.
As artificial intelligence becomes increasingly integrated into programming workflows, I am passionate about researching how foundational computational concepts, such as control flow, abstraction, and programming best practices, can be taught in ways that promote conceptual understanding rather than surface-level tool use. I want students to understand why their code works, regardless of how AI tools are incorporated into their learning process. As a Head TA for Data 6, I contributed to curriculum development and instruction across topics including Python fundamentals (iteration, conditionals, functions), data visualizations, table manipulation, HTML web scraping, API usage, and qualitative coding. I’m currently working publishing our curriculum at dubois-ctds.github.io. To learn more, you can also check out our California Learning Lab project page.
Looking forward, my research interests center on curriculum and pedagogical strategies that support nontraditional learners in developing strong computational identities, integrate structured reflection to help students connect day-to-day work with long-term learning goals, and explore accessible, participatory approaches to teaching computing concepts across disciplines and age groups.
I was recently involved as an undergraduate researcher through UC Berkeley’s Algorithms & Computing for Education (ACE) Lab. My role was studying the efficacy of integrating Human Contexts & Ethics (HCE) concepts into undergraduate data science curriculum. I hold an interest in finding ways to ensure that students think critically about the implications of the technical work that we do.