I study Computer Science and Data Science at UC Berkeley, and I am most interested in work that combines technical seriousness with clarity, craft, and consequence.
Study
Computer Science and Data Science at Berkeley.
Drawn To
Software, research, and technically serious product work.
Bias
Clear systems, careful reasoning, and thoughtful presentation.
My academic path at Berkeley has pushed me to think rigorously about systems, algorithms, data, and product tradeoffs. I enjoy translating that rigor into software that feels refined rather than improvised.
I am especially interested in environments where engineering quality matters: product teams, research settings, and internships where thoughtful implementation and strong communication are both expected.
Technical Direction
Interested in computer science, data science, and machine learning, especially when technical work connects to clear human impact.
Research and Data Experience
Experience spans machine learning research, credit risk modeling, and data analysis through academic programs and internship work.
Interdisciplinary Perspective
I often connect technology with design, ethics, education, and the arts, which shapes how I think about both products and systems.
The habits I try to bring into every project, research setting, and collaborative environment.
I care most about whether technical work genuinely helps people, especially in education, accessibility, and practical decision-making.
Whether in competitions, research, or coursework, I value disciplined learning, careful analysis, and solutions that hold up under scrutiny.
Art, photography, and design influence how I approach technical work: I value clarity, visual judgment, and original thinking.