UC San Diego, class of 2027
Data science and mathematics undergraduate.
I am working toward a PhD in machine learning. My interests sit at the meeting point of applied mathematics and modern deep learning, from probabilistic modeling to building models that are efficient as well as accurate.
About
I am a data science and mathematics double major at UC San Diego, graduating in 2027 with a 3.85 GPA. I am preparing to apply to PhD programs in computer science and applied mathematics, where I want to do machine learning research.
I am especially interested in understanding how models actually work, and how efficient they can be without losing what makes them useful. That interest lives right between the theory in my mathematics classes and the systems side of modern machine learning.
Alongside coursework I spent a year as an instructional assistant teaching mathematics to undergraduates, which sharpened how I explain and reason through ideas.
University of California, San Diego
BS, Data Science and Mathematics
Selected coursework
Interests
Building deep learning systems that are efficient as well as accurate, including model compression and quantization for running large models on modest hardware.
Recovering hidden structure inside messy, real world data, and understanding what the learned states tell us about the system underneath.
Optimization, geometry, and convergence. The theory that decides whether a model learns anything at all, and the reason I want to keep going into research.
Selected work
Built the inference pipeline for a deep learning course competition on mathematical reasoning. We ran a four billion parameter Qwen reasoning model in four bit precision on university GPUs, with no fine tuning, and added self consistency voting, type routed token budgets, and an answer repair pass. The submission reached 0.66 unified accuracy on the private leaderboard.
View repositoryAn interactive world map that visualizes decades of NASA MODIS satellite data to show where vegetation is growing or declining. Users compare a baseline year against a recent one to spot greening, drought stress, and deforestation across the globe.
View repositoryTrained a three state hidden Markov model to sort hourly California electricity demand into low, medium, and high regimes from price and load data, using Baum Welch training and Viterbi decoding. Studied the learned transitions to measure how long demand persists in each regime.
Studied how lenders make decisions using real world loan data. Engineered financial and demographic features to estimate borrower risk and predict interest rates, and modeled disposable income from federal and state taxes to judge repayment capacity.
Built an n gram language model with recursive back off trained on a text corpus, including the full pipeline for tokenization, counting, and probability normalization, validated with unit tests and doctests.
Experience
Mathematics Instructional Assistant
UC San Diego
2025 to 2026 · La Jolla, CA
Ran office hours and discussion sections, breaking problems down to their core ideas and guiding students to reason through them together. Wrote grading rubrics with professors, graded homework and exams against them, and proctored exams while providing accommodations.
Office Clerk
Saddleback Appliances
2022 to 2024 · Lake Forest, CA
Managed appliance and plumbing inventory, organized stock by model number, and processed purchase orders through the store database. Handled customer calls and maintained the store website, including a clearance page to move older products.
Toolkit
Contact
Happy to talk about research, machine learning, or PhD paths.