I am an undergraduate student at the University of California, Berkeley, pursuing degrees in Computer Science and Data Science. I am passionate about leveraging computing to make data-driven decisions across various domains, particularly in biotechnology, business, and education.
My current research interests lie in evaluating large language model robustness, building AI tutoring systems, and developing educational tools in CS-adjacent domains. Overall, I enjoy applying my skills in innovative ways, seeking challenging opportunities, and creating a meaningful impact.
Feel free to check out my resume down below!
Resume
Creating a scalable GenAI tool for commercial decision-making through the Decision Sciences team of the CD&A department under the GCO unit.
Taught 1200+ students in the UC Berkeley's upper-division data science course, Data 100: Principles and Techniques of Data Science.
Led independent projects in computer vision/optical character recognition (OCR), webscraping, and automation testing.
Built educational data science modules through UC Berkeley's Data Science Modules Team for El Camino College courses of varying domains.
Evaluated large language model (LLM) robustness via conversational red-teaming and multi-turn jailbreaking techniques.
Engineered AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for financial forecasting.
Developed data science modules on Simpson's Paradox for UC Berkeley's L&S 22: Sense and Sensibility and Science.
Leveraged Natural Language Processing (NLP) to carry out topic modeling and sentiment analysis on textual data.
