I'm a data scientist — UC San Diego, B.S. Data Science, 2025. I build ML pipelines and analyze large datasets, most recently at Exponential Intelligence. Before that: autonomous vehicles at HDSI, climate research at Scripps, risk modeling at Deloitte.

Most of what I work on sits between messy raw data and a model someone can actually rely on — 70+ years of climate records, autonomous-vehicle perception, LLM survey simulation. See the projects →

Favorite Projects

Education

B.S. Data Science, UC San Diego — Sep 2021 to Jun 2025. Coursework in machine learning, computer vision, algorithms, probability and statistics, linear algebra, recommender systems, and web mining.

Experience

Machine Learning Intern, Exponential Intelligence

Sep 2025 – Present

Irvine, CA

Built an end-to-end LLM pipeline — prompt engineering plus fine-tuned classification models — to generate AI digital twins of survey respondents across political subgroups, using iterative re-prompting to correct response bias; validated with Jensen-Shannon divergence and chi-square tests against real data.

Python · LLM Prompt Engineering · Fine-Tuned Classification Models · Statistical Validation

ML Research Assistant, Halicioğlu Data Science Institute

Sep 2024 – Aug 2025

Autonomous vehicle development

Designed CNN-based perception and control models for autonomous-vehicle navigation on embedded hardware, integrating stereo and RGB-D cameras; trained across 3 prototype configurations on UCSD's Supercomputer Center with MLOps workflows in Weights & Biases; led the team to 1st place at Purdue's national AV competition.

PyTorch · Computer Vision · CNNs · MLOps (Weights & Biases) · Embedded Systems

Data Research Assistant, Scripps Institution of Oceanography

Sep 2022 – Sep 2024

Built Python ETL pipelines to analyze 70+ years of climate records across 60 airports, applying PCA, polynomial, and ridge regression to identify statistically significant correlations; contributed to a peer-reviewed publication linking urbanization to local temperature change.

Python · ETL Pipelines · PCA · Polynomial & Ridge Regression · Climate Data Analysis

Data Science Intern, Deloitte

Mar 2023 – Aug 2023

Trained and evaluated XGBoost and regularized regression models on 50,000+ HHS records to surface predictors of substance-abuse risk, presenting findings to senior leadership via Plotly dashboards.

XGBoost · Regularized Regression · Feature Importance · Plotly

Skills

Languages
Python, SQL, R, JavaScript/TypeScript, Linux
ML & AI
PyTorch, TensorFlow, Pandas, scikit-learn, XGBoost, Hugging Face, LangChain
Tools
AWS, Docker, Spark, Airflow, Databricks, Snowflake, FastAPI, Git

The full history, with a downloadable PDF, is on the résumé. Or say hello.