San Francisco, California, United States
I recently graduated from the University of California, Irvine with a degree in Applied Mathematics and Data Science, where I developed a strong passion for the intersection of mathematics and deep learning. My academic journey has been driven by a desire to understand and solve complex problems that bridge rigorous mathematical theory with practical machine learning applications. This fall, I will be pursuing a Master of Science in Applied and Computational Mathematics at the University of Southern California, where I hope to further explore topics such as high-dimensional probability, numerical analysis, and the mathematical foundations of modern AI. I am always excited to collaborate with others and learn from researchers and practitioners working at the intersection of mathematics, machine learning, and data science.
Applied math research under Professor Angxiu Ni, studied Finite-Time Lyapunov exponents and their relationship with decision boundaries to start working on the path-kernel method
Math tutor in the Irvine region ranging from middle-school to college-level math. Worked online and on-site with clients.
Intern at GotItAI, contributed to two key projects: QA and Model Evaluation [MathGPT]: -Conducted detailed evaluations of an AI model, focusing on accuracy, speed, and effectiveness. -Collaborated with the development team to identify key weaknesses in the model and implement improvements to it, enhancing the AI’s overall performance and user experience. -Provided insights into the AI's impact on student learning, ensuring alignment with learning goals. R&D Data Team [Kaiju (Mathematical LLM)]: -Worked on the pre-trained model in the data team -Utilized SQL for managing and manipulating large datasets. -Leveraged API calls to Gemini to query data from my dataset and evaluate the accuracy of the model. -Engaged in the continuous iteration of data models to improve the pre-trained model