Hello! I am a student at UC Berkeley studying Data Science, with a strong interest in AI, machine learning, and data processing.
I enjoy working with data to solve problems, uncover insights, and build practical applications. I am particularly interested in how intelligent systems can be developed and applied to real-world tasks.
My recent work has included machine learning research on large-scale fraud detection, where I compared multiple approaches, including classical models, ensemble methods, and neural networks, through preprocessing, feature selection, and model evaluation. I am also interested in research and technical work that connects computation with human impact, including accessibility and education.
Recently, I have been exploring personal projects and technical tools related to data workflows and modern infrastructure. These experiences have deepened my interest in applied AI, data-driven technology, and research-informed problem solving.
I am currently seeking opportunities in AI and data-related fields where I can continue learning and contribute to impactful work. The quickest way to reach me is by email at jingxuan.lyu@berkeley.edu.
B.A. in Computer Science and Data Science
Studying computer science and data science with interests in machine learning, software engineering, data-driven problem solving, and thoughtful product execution.
High School Diploma
Built a strong interdisciplinary foundation across computer science, mathematics, economics, research, art, and student leadership through coursework, competitions, and community initiatives.
Summer Program in Introduction to Statistics and Data Science
Summer study in statistics and data science, focused on quantitative reasoning and applied analysis.
Summer Program in Machine Learning and Credit Risk Modeling
Studied machine learning and credit risk modeling, with hands-on Python model building and evaluation.
Research items remain separate from projects and keep their own pages.
Machine Learning Research
A fraud-detection study comparing classical ML models, ensemble methods, and a CNN on large-scale transaction data.
Machine Learning Research
A research project on machine learning models for credit risk prediction, developed through advanced study in algorithms and data.
Selected work across accessibility, community, and education.
Assistive Technology
An accessibility-focused calculator project designed to help visually impaired students perform two-digit multiplication more efficiently.
Community Initiative
A student-led club exploring technology, social computing, aesthetics, and ethics through interdisciplinary discussion.
Education and Outreach
A hands-on chemistry teaching initiative developed during volunteer instruction in Xichuan.